{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"09_Data_Quality","provenance":[{"file_id":"https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/09_Data_Quality.ipynb","timestamp":1608070357286},{"file_id":"https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/09_Data_Quality.ipynb","timestamp":1582855185698}],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"R8C06s763Mie"},"source":["<div align=\"center\">\n","<h1><img width=\"30\" src=\"../res/maiwei.png\">&nbsp;<a href=\"https://github.com/Charmve/computer-vision-in-action\">Coumpter Vision in Action</a></h1>\n","Applied ML · MLOps · Production\n","<br>\n","Join 20K+ developers in learning how to responsibly <a href=\"https://madewithml.com/about/\">deliver value</a> with ML.\n","</div>\n","\n","<br>\n","\n","<div align=\"center\">\n","    <a target=\"_blank\" href=\"https://newsletter.madewithml.com\"><img src=\"https://img.shields.io/badge/Subscribe-20K-brightgreen\"></a>&nbsp;\n","    <a target=\"_blank\" href=\"https://github.com/GokuMohandas/MadeWithML\"><img src=\"https://img.shields.io/github/stars/GokuMohandas/MadeWithML.svg?style=social&label=Star\"></a>&nbsp;\n","    <a target=\"_blank\" href=\"https://www.linkedin.com/in/goku\"><img src=\"https://img.shields.io/badge/style--5eba00.svg?label=LinkedIn&logo=linkedin&style=social\"></a>&nbsp;\n","    <a target=\"_blank\" href=\"https://twitter.com/GokuMohandas\"><img src=\"https://img.shields.io/twitter/follow/GokuMohandas.svg?label=Follow&style=social\"></a>\n","    <p>🔥&nbsp; Among the <a href=\"https://github.com/topics/deep-learning\" target=\"_blank\">top ML</a> repositories on GitHub</p>\n","</div>\n","\n","<br>\n","<hr>"]},{"cell_type":"markdown","metadata":{"id":"ckRIiGksZUnw"},"source":["# Data Quality\n","In this lesson we'll introduce and illusrate the fundamental concept of data quality."]},{"cell_type":"markdown","metadata":{"id":"K-BaYyztanFh"},"source":["<div align=\"left\">\n","<a target=\"_blank\" href=\"https://madewithml.com/courses/ml-foundations/data-quality/\"><img src=\"https://img.shields.io/badge/📖 Read-blog post-9cf\"></a>&nbsp;\n","<a href=\"https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/09_Data_Quality.ipynb\" role=\"button\"><img src=\"https://img.shields.io/static/v1?label=&amp;message=View%20On%20GitHub&amp;color=586069&amp;logo=github&amp;labelColor=2f363d\"></a>&nbsp;\n","<a href=\"https://colab.research.google.com/github/GokuMohandas/MadeWithML/blob/main/notebooks/09_Data_Quality.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"qAE9BjMH8x4q"},"source":["In a nutshell, a machine learning model consumes input data and produces predictions. The quality of the predictions directly corresponds to the quality of data you train the model with; **garbage in, garbage out**. Check out this [article](https://venturebeat.com/2018/06/30/understanding-the-practical-applications-of-business-ai/) on where it makes sense to use AI and how to properly apply it."]},{"cell_type":"markdown","metadata":{"id":"iLYQQyDzFx5c"},"source":["We're going to go through all the concepts with concrete code examples and some synthesized data to train our models on. The task is to determine whether a tumor will be benign (harmless) or malignant (harmful) based on leukocyte (white blood cells) count and blood pressure. This is a synthetic dataset that we created and has no clinical relevance."]},{"cell_type":"markdown","metadata":{"id":"FwJBLYj7HTie"},"source":["# Set up"]},{"cell_type":"code","metadata":{"id":"5o1UIQh6HUih"},"source":["import numpy as np\n","import random"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pe7s9nLDHUlN"},"source":["SEED = 1234"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"V4ax1hLXHUob"},"source":["# Set seed for reproducibility\n","np.random.seed(SEED)\n","random.seed(SEED)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LaHVAEnzKbKl"},"source":["# Full dataset"]},{"cell_type":"markdown","metadata":{"id":"1e1RSFdnKfN2"},"source":["We'll first train a model with the entire dataset. Later we'll remove a subset of the dataset and see the effect it has on our model."]},{"cell_type":"markdown","metadata":{"id":"rgbY9WkklG6T"},"source":["## Data"]},{"cell_type":"markdown","metadata":{"id":"FLH7kzZl8wnf"},"source":["### Load data"]},{"cell_type":"code","metadata":{"id":"5wDazzQdaoy2"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","from pandas.plotting import scatter_matrix"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"y6LNWmoidh8q","colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"status":"ok","timestamp":1608144475885,"user_tz":420,"elapsed":1212,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"9b242558-9b42-40c7-8171-1bf813f8ba47"},"source":["# Load data\n","url = \"https://raw.githubusercontent.com/GokuMohandas/MadeWithML/main/datasets/tumors.csv\"\n","df = pd.read_csv(url, header=0) # load\n","df = df.sample(frac=1).reset_index(drop=True) # shuffle\n","df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>leukocyte_count</th>\n","      <th>blood_pressure</th>\n","      <th>tumor_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>15.335860</td>\n","      <td>14.637535</td>\n","      <td>benign</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9.857535</td>\n","      <td>14.518942</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>17.632579</td>\n","      <td>15.869585</td>\n","      <td>benign</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>18.369174</td>\n","      <td>14.774547</td>\n","      <td>benign</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>14.509367</td>\n","      <td>15.892224</td>\n","      <td>malignant</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   leukocyte_count  blood_pressure tumor_class\n","0        15.335860       14.637535      benign\n","1         9.857535       14.518942   malignant\n","2        17.632579       15.869585      benign\n","3        18.369174       14.774547      benign\n","4        14.509367       15.892224   malignant"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"4yUmtoznqc9r","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144475886,"user_tz":420,"elapsed":1202,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"e0379de3-a50d-4156-cb23-bf5219948b78"},"source":["# Define X and y\n","X = df[['leukocyte_count', 'blood_pressure']].values\n","y = df['tumor_class'].values\n","print (\"X: \", np.shape(X))\n","print (\"y: \", np.shape(y))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X:  (1000, 2)\n","y:  (1000,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"nXFUmnfte6z6","colab":{"base_uri":"https://localhost:8080/","height":279},"executionInfo":{"status":"ok","timestamp":1608144476295,"user_tz":420,"elapsed":1600,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"791e034b-f6d7-430a-8a53-ef67a8e1594c"},"source":["# Plot data\n","colors = {'benign': 'red', 'malignant': 'blue'}\n","plt.scatter(X[:, 0], X[:, 1], c=[colors[_y] for _y in y], s=25, edgecolors='k')\n","plt.xlabel('leukocyte count')\n","plt.ylabel('blood pressure')\n","plt.legend(['malignant ', 'benign'], loc=\"upper right\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"yVD-8qgGwz5l"},"source":["We want to choose features that have strong predictive signal for our task. If you want to improve performance, you need to continuously do feature engineering by collecting and adding new signals. So you may run into a new feature that has high correlation (orthogonal signal) with your existing features but it may still possess some unique signal to boost your predictive performance. "]},{"cell_type":"code","metadata":{"id":"kXXO2ZlGr8Kp","colab":{"base_uri":"https://localhost:8080/","height":429},"executionInfo":{"status":"ok","timestamp":1608144476533,"user_tz":420,"elapsed":1826,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"9f41cedb-d50c-4259-86bb-c22a60e03f85"},"source":["# Correlation matrix\n","scatter_matrix(df, figsize=(5, 5));\n","df.corr()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>leukocyte_count</th>\n","      <th>blood_pressure</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>leukocyte_count</th>\n","      <td>1.000000</td>\n","      <td>-0.162875</td>\n","    </tr>\n","    <tr>\n","      <th>blood_pressure</th>\n","      <td>-0.162875</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 leukocyte_count  blood_pressure\n","leukocyte_count         1.000000       -0.162875\n","blood_pressure         -0.162875        1.000000"]},"metadata":{"tags":[]},"execution_count":7},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 360x360 with 4 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"IcA13EL9MKq0"},"source":["### Split data"]},{"cell_type":"code","metadata":{"id":"v7vmMREZMK6w"},"source":["import collections\n","from sklearn.model_selection import train_test_split"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7ZrZrhNvD5q9"},"source":["TRAIN_SIZE = 0.70\n","VAL_SIZE = 0.15\n","TEST_SIZE = 0.15"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NklqFClX7p9H"},"source":["def train_val_test_split(X, y, train_size):\n","    \"\"\"Split dataset into data splits.\"\"\"\n","    X_train, X_, y_train, y_ = train_test_split(X, y, train_size=TRAIN_SIZE, stratify=y)\n","    X_val, X_test, y_val, y_test = train_test_split(X_, y_, train_size=0.5, stratify=y_)\n","    return X_train, X_val, X_test, y_train, y_val, y_test"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ych4dgkLhRV3","executionInfo":{"status":"ok","timestamp":1608144477465,"user_tz":420,"elapsed":2730,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"fc857b44-701a-48e5-c551-86c7d970f00c"},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X=X, y=y, train_size=TRAIN_SIZE)\n","print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n","print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n","print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n","print (f\"Sample point: {X_train[0]} → {y_train[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X_train: (700, 2), y_train: (700,)\n","X_val: (150, 2), y_val: (150,)\n","X_test: (150, 2), y_test: (150,)\n","Sample point: [14.95081332 14.86441305] → malignant\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_OH53BpMi-e_"},"source":["### Label encoding"]},{"cell_type":"code","metadata":{"id":"oA7kC6GFjD8c"},"source":["from sklearn.preprocessing import LabelEncoder"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wLC4GRM2jEAt"},"source":["# Output vectorizer\n","label_encoder = LabelEncoder()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"LMR1_Du9jEEf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144477467,"user_tz":420,"elapsed":2710,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"06b1f30a-f966-434a-9fc9-7b9c6a8483a4"},"source":["# Fit on train data\n","label_encoder = label_encoder.fit(y_train)\n","classes = list(label_encoder.classes_)\n","print (f\"classes: {classes}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classes: ['benign', 'malignant']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"McIId6GajHty","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144477467,"user_tz":420,"elapsed":2701,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"2b9d54c2-c641-4768-cddf-8c149999a947"},"source":["# Convert labels to tokens\n","print (f\"y_train[0]: {y_train[0]}\")\n","y_train = label_encoder.transform(y_train)\n","y_val = label_encoder.transform(y_val)\n","y_test = label_encoder.transform(y_test)\n","print (f\"y_train[0]: {y_train[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["y_train[0]: malignant\n","y_train[0]: 1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"O7NWNJQxMM-t","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144477468,"user_tz":420,"elapsed":2692,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"edfa1fac-7988-484d-9855-f5dac3db88f3"},"source":["# Class weights\n","counts = np.bincount(y_train)\n","class_weights = {i: 1.0/count for i, count in enumerate(counts)}\n","print (f\"counts: {counts}\\nweights: {class_weights}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["counts: [272 428]\n","weights: {0: 0.003676470588235294, 1: 0.002336448598130841}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"rprTIVQIjLq6"},"source":["### Standardize data"]},{"cell_type":"code","metadata":{"id":"Vk-dk3X0jLxR"},"source":["from sklearn.preprocessing import StandardScaler"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GDTGSoERjL0B"},"source":["# Standardize the data (mean=0, std=1) using training data\n","X_scaler = StandardScaler().fit(X_train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7CcSCO4bjL2n"},"source":["# Apply scaler on training and test data (don't standardize outputs for classification)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hzQ5s5eqjPMy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144477470,"user_tz":420,"elapsed":2663,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"5939484b-9072-47e3-a7bf-bc3187496f60"},"source":["# Check (means should be ~0 and std should be ~1)\n","print (f\"X_test[0]: mean: {np.mean(X_test[:, 0], axis=0):.1f}, std: {np.std(X_test[:, 0], axis=0):.1f}\")\n","print (f\"X_test[1]: mean: {np.mean(X_test[:, 1], axis=0):.1f}, std: {np.std(X_test[:, 1], axis=0):.1f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X_test[0]: mean: -0.1, std: 1.0\n","X_test[1]: mean: -0.1, std: 1.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"YZwkrokjlL81"},"source":["## Modeling"]},{"cell_type":"code","metadata":{"id":"OSmsF8b8A_r-"},"source":["import torch"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"nF_-_GQTHMuL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144480176,"user_tz":420,"elapsed":5352,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"dd57efdb-23f9-4b9e-a092-3dcc6295634e"},"source":["# Set seed for reproducibility\n","torch.manual_seed(SEED)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<torch._C.Generator at 0x7f4896c41d08>"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"owLnzReJJdpj"},"source":["### Model"]},{"cell_type":"code","metadata":{"id":"F6PCHuW0rBco"},"source":["from torch import nn\n","import torch.nn.functional as F"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6NiIKIzKHRMa"},"source":["INPUT_DIM = 2 # X is 2-dimensional\n","HIDDEN_DIM = 100\n","NUM_CLASSES = 2"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pfDgN3F-GCKP"},"source":["class MLP(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, num_classes):\n","        super(MLP, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, hidden_dim)\n","        self.fc2 = nn.Linear(hidden_dim, num_classes)\n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        z = F.relu(self.fc1(x_in)) # ReLU activaton function added!\n","        y_pred = self.fc2(z)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qaYgpFapGCRg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144480178,"user_tz":420,"elapsed":5325,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"978dfb40-b192-4c6e-c065-8e37155ce9ef"},"source":["# Initialize model\n","model = MLP(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)\n","print (model.named_parameters)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of MLP(\n","  (fc1): Linear(in_features=2, out_features=100, bias=True)\n","  (fc2): Linear(in_features=100, out_features=2, bias=True)\n",")>\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"fkSY4lj5Ibe0"},"source":["### Training"]},{"cell_type":"code","metadata":{"id":"bvAvgInrsXtp"},"source":["from torch.optim import Adam"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_uzT5WdgrPcV"},"source":["LEARNING_RATE = 1e-3\n","NUM_EPOCHS = 5\n","BATCH_SIZE = 32"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"w5zvcjQ8GCHF"},"source":["# Define Loss\n","class_weights_tensor = torch.Tensor(list(class_weights.values()))\n","loss_fn = nn.CrossEntropyLoss(weight=class_weights_tensor)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"q5t1LqwFrMej"},"source":["# Accuracy\n","def accuracy_fn(y_pred, y_true):\n","    n_correct = torch.eq(y_pred, y_true).sum().item()\n","    accuracy = (n_correct / len(y_pred)) * 100\n","    return accuracy"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"CA3un62trOZi"},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tEfrIX0tGCBU"},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NXyUnTDlGB_f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144480857,"user_tz":420,"elapsed":5955,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"20bb8dfa-2067-4b68-d804-1298650e917d"},"source":["# Training\n","for epoch in range(NUM_EPOCHS*10):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%10==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 0.70, accuracy: 48.4\n","Epoch: 10 | loss: 0.54, accuracy: 92.3\n","Epoch: 20 | loss: 0.43, accuracy: 97.4\n","Epoch: 30 | loss: 0.35, accuracy: 97.4\n","Epoch: 40 | loss: 0.30, accuracy: 97.4\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Gp-2SuGlJCd8"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"id":"ZIq3_RBwgwzT"},"source":["import json\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import precision_recall_fscore_support"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rfJfD4Wvgw2W"},"source":["def get_performance(y_true, y_pred, classes):\n","    \"\"\"Per-class performance metrics.\"\"\"\n","    # Performance\n","    performance = {\"overall\": {}, \"class\": {}}\n","\n","    # Overall performance\n","    metrics = precision_recall_fscore_support(y_true, y_pred, average=\"weighted\")\n","    performance[\"overall\"][\"precision\"] = metrics[0]\n","    performance[\"overall\"][\"recall\"] = metrics[1]\n","    performance[\"overall\"][\"f1\"] = metrics[2]\n","    performance[\"overall\"][\"num_samples\"] = np.float64(len(y_true))\n","\n","    # Per-class performance\n","    metrics = precision_recall_fscore_support(y_true, y_pred, average=None)\n","    for i in range(len(classes)):\n","        performance[\"class\"][classes[i]] = {\n","            \"precision\": metrics[0][i],\n","            \"recall\": metrics[1][i],\n","            \"f1\": metrics[2][i],\n","            \"num_samples\": np.float64(metrics[3][i]),\n","        }\n","\n","    return performance"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5MM9gNg6I4eG"},"source":["# Predictions\n","y_prob = model(X_test, apply_softmax=True)\n","y_pred = y_prob.max(dim=1)[1]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5sRAXVwPOSBs","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144480860,"user_tz":420,"elapsed":5929,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"89b9f7e9-c397-493a-a8bc-59050a2a5a93"},"source":["# Performance report\n","performance = get_performance(y_true=y_test, y_pred=y_pred, classes=classes)\n","print (json.dumps(performance, indent=2))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["{\n","  \"overall\": {\n","    \"precision\": 0.9663271080928126,\n","    \"recall\": 0.9632683658170915,\n","    \"f1\": 0.9647473560517039,\n","    \"num_samples\": 150.0\n","  },\n","  \"class\": {\n","    \"benign\": {\n","      \"precision\": 0.9649122807017544,\n","      \"recall\": 0.9482758620689655,\n","      \"f1\": 0.9565217391304347,\n","      \"num_samples\": 58.0\n","    },\n","    \"malignant\": {\n","      \"precision\": 0.967741935483871,\n","      \"recall\": 0.9782608695652174,\n","      \"f1\": 0.972972972972973,\n","      \"num_samples\": 92.0\n","    }\n","  }\n","}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ViwfNFOYRDkm"},"source":["We're going to plot a point, which we know belongs to the malignant tumor class. Our well trained model here would accurately predict that it is indeed a malignant tumor!"]},{"cell_type":"code","metadata":{"id":"q9qZ6Fijgvfd"},"source":["def plot_multiclass_decision_boundary(model, X, y):\n","    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n","    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n","    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), np.linspace(y_min, y_max, 101))\n","    cmap = plt.cm.Spectral\n","    \n","    X_test = torch.from_numpy(np.c_[xx.ravel(), yy.ravel()]).float()\n","    y_pred = model(X_test, apply_softmax=True)\n","    _, y_pred = y_pred.max(dim=1)\n","    y_pred = y_pred.reshape(xx.shape)\n","    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n","    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n","    plt.xlim(xx.min(), xx.max())\n","    plt.ylim(yy.min(), yy.max())"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bzFb90SJOmI2","colab":{"base_uri":"https://localhost:8080/","height":336},"executionInfo":{"status":"ok","timestamp":1608144481128,"user_tz":420,"elapsed":6180,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"5966696c-a66c-4a51-a4a7-7616d3fd21bf"},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(8,5))\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","\n","# Sample point near the decision boundary\n","mean_leukocyte_count, mean_blood_pressure = X_scaler.transform(\n","    [[np.mean(df.leukocyte_count), np.mean(df.blood_pressure)]])[0]\n","plt.scatter(mean_leukocyte_count+0.05, mean_blood_pressure-0.05, s=200, \n","            c='b', edgecolor='w', linewidth=2)\n","\n","# Annotate\n","plt.annotate('true: malignant,\\npred: malignant',\n","             color='white',\n","             xy=(mean_leukocyte_count, mean_blood_pressure),\n","             xytext=(0.4, 0.65),\n","             textcoords='figure fraction',\n","             fontsize=16,\n","             arrowprops=dict(facecolor='white', shrink=0.1))\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x360 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"o231eJaQPi5E"},"source":["Great! We received great performances on both our train and test data splits. We're going to use this dataset to show the importance of data quality."]},{"cell_type":"markdown","metadata":{"id":"pZ3rnGH8PtBu"},"source":["# Reduced dataset"]},{"cell_type":"markdown","metadata":{"id":"ONRP3WQgR3zc"},"source":["Let's remove some training data near the decision boundary and see how robust the model is now."]},{"cell_type":"markdown","metadata":{"id":"IoiDkq7ClP2Q"},"source":["## Data"]},{"cell_type":"markdown","metadata":{"id":"3E8MUvGCK0zW"},"source":["### Load data"]},{"cell_type":"code","metadata":{"id":"sU69PjH3Z4bm","colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"status":"ok","timestamp":1608144481132,"user_tz":420,"elapsed":6174,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"9db28cbc-f7ea-4369-9e25-c684075b1710"},"source":["# Raw reduced data\n","url = \"https://raw.githubusercontent.com/GokuMohandas/MadeWithML/main/datasets/tumors_reduced.csv\"\n","df_reduced = pd.read_csv(url, header=0) # load\n","df_reduced = df_reduced.sample(frac=1).reset_index(drop=True) # shuffle\n","df_reduced.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>leukocyte_count</th>\n","      <th>blood_pressure</th>\n","      <th>tumor_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>16.795186</td>\n","      <td>14.434741</td>\n","      <td>benign</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>13.472969</td>\n","      <td>15.250393</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>9.840450</td>\n","      <td>16.434717</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>16.390730</td>\n","      <td>14.419258</td>\n","      <td>benign</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>13.367974</td>\n","      <td>15.741790</td>\n","      <td>malignant</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   leukocyte_count  blood_pressure tumor_class\n","0        16.795186       14.434741      benign\n","1        13.472969       15.250393   malignant\n","2         9.840450       16.434717   malignant\n","3        16.390730       14.419258      benign\n","4        13.367974       15.741790   malignant"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"code","metadata":{"id":"pWh45mgZqqaO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144481136,"user_tz":420,"elapsed":6167,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"8a6b2779-4b23-43e8-ca59-640254f89fce"},"source":["# Define X and y\n","X = df_reduced[['leukocyte_count', 'blood_pressure']].values\n","y = df_reduced['tumor_class'].values\n","print (\"X: \", np.shape(X))\n","print (\"y: \", np.shape(y))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X:  (720, 2)\n","y:  (720,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1OwgEJSsZ4g5","colab":{"base_uri":"https://localhost:8080/","height":279},"executionInfo":{"status":"ok","timestamp":1608144481480,"user_tz":420,"elapsed":6501,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"5d8d7b98-625b-40d8-987d-0275811ceb29"},"source":["# Plot data\n","colors = {'benign': 'red', 'malignant': 'blue'}\n","plt.scatter(X[:, 0], X[:, 1], c=[colors[_y] for _y in y], s=25, edgecolors='k')\n","plt.xlabel('leukocyte count')\n","plt.ylabel('blood pressure')\n","plt.legend(['malignant ', 'benign'], loc=\"upper right\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydd3gU1dfHv7N9Z3bTK6EkECCU0BM6hC5SRYpSBQWRoohIUZBqAwQFBUQp8iodBKRJlyKGIr35QwLSa2jpm/2+f8wk7CYBAklIAvN5nnnIztw6y55777nnniOQhIqKiorKi4MmtxugoqKiovJsUQW/ioqKyguGKvhVVFRUXjBUwa+ioqLygqEKfhUVFZUXDF1uNyAzeHl5MTAwMLeboaKiopKv2L9//w2S3mnv5wvBHxgYiH379uV2M1RUVFTyFYIgnMvovqrqUVFRUXnBUAW/ioqKyguGKvhVVFRUXjDyhY5fRUUl/5KUlIQLFy4gPj4+t5vy3GIymVCwYEHo9fpMpVcFv4qKSo5y4cIFWK1WBAYGQhCE3G7OcwdJ3Lx5ExcuXEBQUFCm8qiqHpV8T0xMDMaM+RRVqjTAa6/1wLFjx3K7SSoOxMfHw9PTUxX6OYQgCPD09HyiFZU641fJ15BE48av4O+/LYiPH4QDBw5jzZp62L9/J0qUKJHl8rdv345Fi36Fu7srevbsjiJFimRDq188VKGfszzp+1Vn/Cp5FpI4cuQITpw4gYe5D//7779x6NC/iI9fAqAp7PYhiIt7B1999V2W6x8/fjKaNu2K6dN9MGHCHZQtG4ZDhw5luVwVldxGFfwqeZKzZ8+iePEKqF69NapUaYKKFWvj2rVr6dJduXIFWm1RANrUe8nJxXD+/JUs1R8TE4NRo8YiNnYryGFITJyM+/dHYPDgMVkqVyX/sW3bNjRv3hwAsGrVKnzxxRfPrO6DBw9i7dq12V6uKvhV8iQdO/ZCVFQHxMScRmzsWRw7FobevQemS1erVi3YbPsB7FHu3IckTUf79k2zVP/ly5eh0bgAcNwsq40TJ05lqVyVx2O327F3717s3bsXdrs9t5vjRMuWLTF06NBnVp8q+FVeGBITExEZuQ12+wcABAAa2GyDsX79mnRpXV1dMX/+XEjSy3BxqQKTKRCvvBKKLl26ZKkNRYoUgV6fCGB36j2tdgHq1KmepXJVHs25c+dQrlgxdK1fH13r10f54GCcO5eh14FMc/bsWYSEhOCNN95AiRIl0KlTJ2zatAk1a9ZE8eLFsWePPGnYs2cPqlevjooVK6JGjRo4dSr9ID937lz069cPAPDvv/+iWrVqCA0NxfDhw2GxWADIK4SIiAi0bdsWISEh6NSpU6qqcsyYMQgLC0PZsmXRq1ev1PsREREYMmQIwsPDUaJECezYsQOJiYn45JNPsGjRIlSoUAGLFi3K0ntwgmSevypXrkyVF4fk5GRKkieB/xGgcv3JgICSD80TExPDXbt28b///su2dqxcuZJmszslqT2t1ggWKBDMCxcuZFv5LwrHjx/PdNrmEREcq9HQDtAOcKxWy+YREVmqPyoqilqtlocPH2ZycjIrVarE7t270263c8WKFWzVqhVJ8s6dO0xKSiJJbty4kW3atCFJbt26lc2aNSNJzpkzh3379iVJNmvWjPPnzydJTp8+nZIkpaZ3cXHh+fPnmZyczGrVqnHHjh0kyZs3b6a2q3Pnzly1ahVJsm7duhw4cCBJcs2aNWzQoEG6+h5HRu8ZwD5mIFPVGb9KnkOj0WDw4EEQxVcBLAewEKLYBaNGDX5oHlEUUaNGDRQqVCjb2tGyZUtERZ3At982xbx57+HMmaMICAjItvJV0rN++3YMsNshQF7rDUhOxvrt27NcblBQEEJDQ6HRaFCmTBk0aNAAgiAgNDQUZ8+eBQDcuXMH7dq1Q9myZfH+++8/1ix49+7daNeuHQCgY8eOTs/Cw8NRsGBBaDQaVKhQIbWOrVu3omrVqggNDcWWLVuc6mjTpg0AoHLlyqnpcwrVnFMlTzJixBAEBQVg2rTvodfrMXDgRLRu3fqZt8PX1xdvvPHGM6/3RaWAhwf+uXEDlZTPpwAEeHhkuVyj0Zj6t0ajSf2s0Whgs9kAACNGjEC9evXw66+/4uzZs4iIiMiW+rRaLWw2G+Lj49GnTx/s27cPhQoVwqhRo5xs71PypKTPSdQZv0qW4UNMLbOCIAjo0qULdu/+Hdu3r84Voa/y7Pl4zBi0F0X8BOAnAB1EER+PHftM6r5z507qim7u3LmPTV+tWjUsW7YMALBw4cLHpk8R8l5eXrh//z6WLl362DxWqxX37t17bLonJccEvyAIswVBuCYIwlGHexUEQfhLEISDgiDsEwQhPKfqV8l5Dh8+jIoV60Cr1cLfPxgLFmTj5pPKC0mvd97B5IULsapRI6xq1AiTFy5Ez969n0ndgwcPxrBhw1CxYsVMzbi//vprTJo0CeXKlcPp06fh6ur6yPRubm7o2bMnypYtiyZNmiAsLOyxddSrVw/Hjx/PP5u7AOoAqATgqMO9DQCaKn+/DGBbZspSN3fzHjExMXR39ycwk0ACgZ0URX/u3r07t5umksd4ks3d/ERMTAztdjtJcsGCBWzZsmWutudJNndzTMdPcrsgCIFpbwNwUf52BXApp+pXyVk2btyI5OTSAHoqd2oiNvZ1jBs3HsuWLXDScaqoPI/s378f/fr1A0m4ublh9uzZud2kTPOsN3cHAPhdEISJkNVMNR6WUBCEXgB6AUDhwoWfTetUHondbodGI2sHZd8gKYdrYgC0BXAAGzZ4wc8vEKtXL0XNmjVzqaUqKjlP7dq1860Lj2e9ufsOgPdJFgLwPoBZD0tIcibJKiSreHunixWs8gzZt28fypSpBq1WiwIFSmDZsuVo3Lgx9Pr/QRCmAhgNQAJwAUlJR3H79ky0bt0RycnJudxylbwCc8AAQOUBT/p+n7Xg7wbZMBsAlgBQN3fzOHfu3EGDBs1w/Hg/AEm4fPkHdO3aB6dOncL27b8jPHwVgJkA3sODBWQLxMfrcfLkyVxrd05w69YtLFq0CL///nuOm9s9T5hMJty8eVMV/jkEFX/8JpMp03metarnEoC6ALYBqA/gf8+4fpUnZM2aNSCrAeis3KmL+PhemDv3F0yePB5//bURDRu2xubN/wCoraS5g6Skm/Dx8cmdRucA69evR5s2HaHT1QZwFV5eH2D37s3w9fXN7ableQoWLIgLFy7g+vXrud2U55aUCFyZJccEvyAICwBEAPASBOECgJGQdwK/EQRBByAeig5fJe8i6/SdVTaC8EDXDwCjRg3C7t1tEBcXAzIAojgJ7du/judFRWez2dCx41uIi1sB2VgNiIsbgGHDRmP27Gm527h8gF6vz3RkKJVnQ05a9bz+kEeVc6pOleynWbNm0Gjeh6zOeR3AThiNM9Gjx9bUNLVq1cLWravx5Zff4urV39G5c1f07PlWbjU524mKikJioh4pQh8AbLau2Ly5W+41SkUlC6guG1QeidVqxfbtv+PNNwfg4MH3ULhwSUydOhdlypRxShceHo5ly+blUiuzn6ioKAwf/in27DmI8uVLISnpFoALAFKW05EoWbJ4LrZQReXpEfLDhkuVKlW4b9++3G6GygvC7du3ERwciujoHrDbCeAbyC7DJACDoNNdhdE4C3/8sR6VK6sLWJW8iyAI+0lWSXtf9dWjopKGhQsXIi6uJuz21wDMALALwG0AI6DVjkGnTrdw8OBuVeir5FtUVY/Kc8WdO3dgs9ng6en51GXcvHkTCQkBAFZC3tcoqzzpDUE4gZAQfwQHB2dDa1VUcgd1xq+Srzl//jxOnz6NmJgYtGnTGT4+hVCgQFHUrNkEV69efaoyW7RoAYPhFwAJAJzj/BoM1+Dm5pb1hquo5CKq4FfJVyQmJiI+Ph537txBREQzlChRCeXL10XBgqWwdm00EhMvITHxOvbsCcVrrz2dZVG5cuUwfvxImEyTAfwG4FMAhyEIn8Ng+AMdOnTIzi6pqDxzVMGvki9ISEhA9+59YLV6wGp1R5kyVbF7tzfi4y8iNvY8bt9+EwkJtwFYABhgs43Fzp2bERsbm6nyk5KSkJCQgEuXLqF//0H4+ecV6N27F375ZSaaNz+MggVfR8uWR7Fnzx9wd3fP0b6qqOQ0qo5fJV8wdOhILFx4HomJZwGYcPFiIQAjABiUFMMAfAHgHgArgHtITqbTQbOMSEhIQJ8+H+Dnn+fCbk+GRiPBbm8Hm+1dHDjwKwICPsOJE/tVb6MqzxXqjF8lXzBv3i+Ij58AwAvyrD4Azl69b0D2+r0HwD4AnaHVumPnzp2PLPejj0ZhwYIoJCZGwWa7hsTEVrDZbgBohsTEH3Hjhi9WrlyZI31SUcktVMGvkichiYsXLyImJgYAoNXqAcQ5pHgbwGsAVgDYCJ2uJYAgAB8A6AKgNsh2OHDgwCPr+emn+YiLmwjAG/JK4RsAawDIKqLExBK4dEkNG6HyfKGqelTyHAcOHECbNl1w5cpVkImoXbsWbt68CqAHgDkARJhMmxAWVgqxsVMQH5+AsLDyWLz4GGJjdwHQAkiEyVQJFSpMfmRdOp0estuoFBIhz4c0AP6DRrMcjRtvzTCvikp+RZ3xq+QpkpKS0LhxK5w9OwTx8VuQkOCPTZsiYbdrABgBvAIgHKGht7Fhw2rs27cFR4/uwg8/fI9KlVxhsdSAIAyD0VgR5cr5oX79+qllk8TUqdPg5xcMSfLAa6/1wFtvdYYo9gFwCMBpaLXdoNFo4eraCCZTeYwZMwy+vr44duwYkpKScuelqKhkM6rgV8lT7N27FwkJngA6AWgHYBCA6wAuQ47W2RvAEiQlCU7+x3U6HbZuXYP69QtBq50KQfDGoUP/ok2bzti4cSOmTZuGsWPHYejQ73D16kLExh7Br7/qsW3bbgwd2gre3q3h6lobvXuXwOnTB7FixVicO3cKUVHnERBQDNWqvQI/vyBs3rw5F96Kiko2k1Eg3rx2qcHWXxwOHTpESQokcJRAEQJ2AlSuLQSqURAmsm3brunyHjx4kGZzAQI3lfR3qdH40GQKodnck4LgReBVh/ISaDJ58vz58yTJOXN+YkhIOH18irFJk2YcPnw4RbGiQ3mbKEmevH///rN+LXmGmzdv8p9//mFycnJuN0UlE+AhwdbVGb/KQ0lKSsLChQsxcOBg/PLLL0hMTMzxOkNDQxESUhh6/TgAd+Gsf78M4B5E8QuMGPFBurw7d+4E2QyAh3JnJez2IMTHH0Fc3EyQ/4Psd+ew8lwLQdDBZrNh9uy56Nv3M5w8ORbXrs3H77/HYty4aYiN9QCQYrffAFptWezYsSMnup6nSU5ORv+33kLRAgXQsGJFlCxYEHv27MntZqk8LRmNBnntUmf8zx6bzcaaNRtTkmoS+JSSVIdhYRFMTEzM8bpv3brFSpVqEXAl0IjATgKLKAiebNq0JU+cOJFhvt9//50WS3kCNmWG3o/AJIcZPgm8RuBTAjHUaj9ihQq1SJLBwRWVFUVKulsErAQCCOxS7iVTkoozMjIyx99BXmPG9OmsIYq8BdAOcAnAAA8PXrp0iZGRkbx7925uN1ElA/CQGX+uC/XMXKrgf/asWrWKFksVByGaTIulBhcvXpyp/MnJyVy5ciX79BnASZMm89atW5muOzExkaLoQeAEgVEEKhEIY0BA8cfWWb16Q4pifQLTqNNVoiA0dlAXJVCvD6TBYKFGo2dERHNevHiRJOnnF0zgkIPgTyTgQqALgbYEdtFg6MKKFWvRbrdnui/PCy9Vr87lziMoSxkMtBgMrOTiQnezmXNmzcrtZqqk4WGCX1X1qGTI0aNHERcXAdk0EgA0uH+/Po4dO5ap/F27vo1XX+2DadPmY+DAsShYsCQuX76cqbyxsbFITIwHUBxyxM79ABbj/v37j8yn0WiwdetqTJnSEZ06HcCYMe0RFHQZotgKwARIUm3Ur18RsbG3ERd3HzNnTsLq1auxbt06dOjwCvT6jwHcB2AD8BmAijCb/0alSpdRvPh76Nu3ALZuXQ1BEDLVj+cJdy8vXHH4bANwNTER6xITsf/uXfwZF4cP+vbF2bNnc6mFKk9ERqNBXrvUGf+zZ9OmTZSkUgTiUmfLFkt5rl69+rF5T5w4Qa3WnUB9AkeUqxbDw+tmuv7SpcMJfK/UbadON4Dt2nV74n7cv3+f06fPYJ8+A7ho0SImJSWRJCdO/JomkxdFsRut1iosUyaczZq1pSCYCVgI+BDwoJdXUf7zzz9PXO/zxq5du+grivw/gH8BfFWrZek0K4BuosgZM2bkdlNVHICq6lF5Eux2O9u06UxJKkGjsS8lKYTNmrXLlDXHr7/+SkAkEOUgF6Ko1YqZrv/o0aP08Qmk1VqBFktxlipVhVeuXMlKl1K5cuUKTSY3Av+lDiwm06v89NMvePLkSZrNHhSETwjso0Yzhu7uBVJVVZcuXeL48RM4YsRIHjx4MFvak1/YsmULm9aqxQpFi7J+rVrsajQ6Cf46ViuXL1+e281UcUAV/CpPjN1u5/bt2/n1119z69atmdZtnz9/noCewBUHuXCVWq3piepPSkrizp07uW/fvmzVq69du5YuLo3SbPouZr16rTlz5kxKUlunZ6L4GqdNm8ZDhw7RavWh0fgWtdrBNJt9OWvWnGxrV37iypUr9Hdz4wiNhpsB9tbrWTowkAkJCbndtGxnzZo1rFWuHIO8vfl2t268ceNGbjcp0zxM8OdYzF1BEGYDaA7gGsmyyr1FAEoqSdwA3CZZ4XFlqTF38x8VKtTEoUN+AGYDAAShJ9q1k7Bo0Zwcrffo0aPYvHkzChYsqARUkb13Hjt2DJ99NhmnT/+HunUrYurUOYiP/xeyfx5Ar++Pd9+1wsfHDcOHX0BS0pTUMrXaAXjnnWScOHEemzc3ANBfeXIEVmtD3LhxPrWeF4kzZ85g/OjROH7gAKrWq4chI0bAy8srV9oSFxeH5ORkWCyWbC13x44d6PDSS5gWG4sQAFP0ehwqXRo7DxzIF3s9D4u5m2OzdAB1AFQCcPQhz78C8ElmylJn/PmPe/fusVGj1tRojNRqzWzTpjPv3buX7fXcvXuX8+bN44wZMzh06Aiazb40GnvTaq3D4sUrMDo6midPnqTF4k2N5nMCv9Fsbk4/v+KUpLIEvqTJ1JXe3kV46dIlHj9+nGazN4Fjyoz/BAFXms0B1OlSLI0erAbMZj/+999/2d4vlcwRHx/PXl26UDIYaNbp2LJ+fV6/fv2h6aOiovjjjz9y7dq1qfs9j6Jjq1ac5vCFJwMsKkn8+++/s7MbOQYeMuPPMSdtJLcLghCY0TNBHirbA6if0XOV/I/FYsGGDb8iPl4+gOXoXiG7OHXqFKpXr4+kpDAkJ+sQF7cWwP8ABCAhgUhM7ILJk6fi4sWriIvrC7t9KAAgLu4lCEIxfPFFfxw/fhpFi5ZDjx6T4OnpCX9/f0yZ8iUGDKiDmBgDZC+dExEX1x0aTSkAywF8pLTgLyQlxeLw4cMoVKhQtvdP5fGMHDYMl5cuxX+JiTADGLpjB9587TWs3LQpXdofv/8eQwYMQFOtFv/TaDAiIACbdu9+ZCjNmLt34fhUA8BNq031GptvyWg0yK4LQCAymPFDXg1kOBI5pOkF2bH6vsKFC+fckKjyzLh16xaHDBnOypXrs0ePPjxz5kyWymvYsDUFYaIyGVtPoE4avf0y1qnTgg0btiHwi9MzF5cIrlu37qFl79u3j6JYmECMQ74F1Ggs1OubEuhE+YBZf4piIS5ZsjRLfVF5Oop4efGYwxcbC1DU69OtLqOjo+lmMvEfJZ0dYCejkSM//viR5f/8888sL0m8oMz2fwJYyMvrmRxkzA4eJmdzy47/dQALHpWA5EySVUhW8fb2fkbNUskpbDYbqldviK+/Po/9+wfjp5/cUalSTVy5cuXxmR/Cvn17QLZVPpUCcBRAdOpzg2ETwsPLol27lyCK05HiYx/YA5vtEGrWrPnQsosUKYLk5DuQA66ncA+1atUGuQ1yIJj+AHwRG/sxRowY/9T9UHl6TEYjHOfecQAEQYBWq3VKd+jQIZQ2GlFc+SwAeD0hAX9u3PjI8jt27IhW/fqhjMkEL6MRXxUtilUbN0Kv12dnN549GY0G2XUhgxk/5BgAVwEUzGw5L6KO/8KFC5w/fz537dqVZ0+K2u32TLdtzZo1tFqr0tHpmtHYi2PGfPrU9Ver1ojAbIcZ+RuKi4WRFMV29PMryitXrjApKYnt2nWjyeRNF5dwSpInV6xY+djye/V6l5IUTmA+BWE8RdGb69ato04nEfAm0IPA+wQ8abF4P3U/VJ6eyRMmsLIoMhLgMYAtTCb27Nw5XboLFy7Q3WjkLYfVwVCdjv179cpUPbGxsbxy5Uqe/S0+DOSGOedDBP9LAP54knJeNME/ffpMmkzutFrbUJJKskaNRoyNjc3tZqWSmJjIfv0G0Wx2pcEgslOntx67cTtr1ixKUqc0qpjx7NWr/1O14dy5c+zTpy/1epF6fWdqNMNoMvlw6NBh/PDDofz22+94+/ZtpzxRUVHcvn07Y2JiMiwzKSmJI0aMoa9vMfr5BXPUqE85a9Ys1q/fmh07vpm6oSeK/gSmOfRjDw0Gt0xtFr4o3Lx5k/PmzePixYsf+r6zA7vdzskTJrBkQACLeHlxyPvvMz4+PsO0g/r1YylJ4niAbxmNLODuzrNnz+ZY2/ICz1zwQ1blXAaQBOACgDeV+3MB9H6Ssl4kwX/t2jUaja4ETitCxUaz+WVOmjQ5t5uWSqdO3ajTBROYSuA/Go2vs337N2iz2bh8+XL27z+Q06fPcBoMzp07R5PJg8BJpV83KUkhXLt27RPXv337dkqSFw2GPtTp+lOnc2W7dh2yfKDq/feHUhTrEThA4G+KYm0OHfpJ6vPo6Gh27txTOdn7r9MgZjR6ZNsBs/zOtm3b6ClJbGOxsInVyoKeno88/Wyz2Z7JxMZut3PNmjXs36sXP//00xfi+8qVGX92XS+S4F+zZk0Gh4sWsGHDNg/NY7PZuGbNGn711Vc5rhoaNOhjRc3xPoEGBMoROEu93swWLdpTkioR+Jyi2JJFi4Y6eW384YfZNJvd6OISTpPJnQMGDHnitl6+fJkeHkEEFju8n99ZsGDII/MdP36cPXr0YePGr3LWrFm02WxOz+12O81mVwLnHMr9H63WByqcGjUaU69/i0BjOnv93ElPz0LpynwRsdvtLFWoEFc5/AceLwhs3bBhhmnHffIJPSSJBq2WEVWq8PTp07nQ6ucXVfDnE06cOEGz2Y9AbKpg0esH8r33PswwfUJCAmvUaESLpRINhncpSUX5xhu9c6RtUVFRNJk8CdxgiqsD4HUCn1CnM9FsDuQD3z6k2fwqJ0/+xqmM27dvc8eOHbx06dIT13/nzh36+gYRMBK47SB4kwloGBISxiJFQjly5Fgnqws5uIsXtdrRBH6mJFVnly7Oul273U6dzuTQNxK4TKPR6tB3b8peO48R8CPQhoLQjWazB5cv//Up3ujzR3R0NC16Pe0Ogv88QF8Xl3Rpf/rpJ5aXJP4LMAHglxoNywYFZXniYrfbOfXrrxns50dvi4WNa9fmV199xXPnzmWp3PyIKvjzEW3bdqUkVSUwnUZjH7q7F0iNEpWW//u//6Mk1eED98n3KIqFuXfv3mxv14oVK+ji8nKa1ch8ajTFWbVqRBodfjKBMaxTpwnj4uKypf7p06dTFNsSqE3gJ4e6VlIQ3An8TmA3jcYGLFWqCvv0GcC9e/eyTZvODmaf8jsyGNx44cIFp/LbtetGg+ENAvcI3KXR2JmdO/ckSZ46dYqiGKD0i5T99b9Lf/8gRkVFZUv/ngdsNhv93dx4wOE/yXyAdSpWTJe2cbVqXOaQzg6wmCQ9UmX36/LlDCtZkv6uruzWrl2G6ppZP/zAsqLIPQDPAnwdYLBGQw+zmYsXLcrW/uZ1VMGfj7DZbPz555/ZoUN3fvzxyEfOjt9++10CE52Esdn8FqdPn87z58/z5ZfbURTdWbRoeS5evCRL7Tpz5gxNJi9F6KXU147ly1flvn37lJXKLQI7CBQj4EHATJPJnfv373cqKz4+npGRkU906vWjj4ZTEIYT2KOomzopKw6RwGSHNt1Q7rWhHExFoOzT/5hDmmLpPI3evn2bzZt3UDaMRbZu3TFVVWW32xkSUpkazVgCCQTOUxTD+d1307P0Tp9H5s6eTX9R5GiNhh/qdPQSRf7xxx/p0jWvU4e/pDkVW1AUefz48QzL3bp1KwuIItcCjAL4vk7HyiEh6VYIVUuV4gaHcuMAugHcANDHan3o5u/ziCr4nwNOnz7N9u3fYLFildi5c0+eO3eOM2f+QEl6iQ/MJOMpScX5xx9/MCioDLXa4QQuE9hIUSzAbdu2ZakN7747mJJUlIIwlJL0EoOCyqR6rhwwYAjNZn9F2K5S2nSBQAkGBBRLLWPTpk10dfWli0t5mkyebN/+jVSLmGPHjrFatUbU680MCirH3377LTXfjh07KElBBK4p1+fU6610dS1AOUZvym89gYCZD6JnxVPeiC6o/L2TgJVff/11hn28d+9ehnF1z549y6pVGyhqLVd++OFwNfbsQ9izZw8/6N+fHw0ezFOnTmWYZuXKlQwSRW5X1EH99XrWqlDhoaqejq1acXqaFUIJiyXd6rZScDC3O6RLAugB8CLAohbLQ9vztNjtdm7YsIGjR4/m4sWL89ThLlXw53Nu3LhBd/cC1GjGEPiLWu1H9PEpwmvXrrFMmXBKUgMCoylJFdi8eXvu2LGDVmsonYOVf8s2bbpkqR0pHjtHjx7NuXPnpjPVmzp1qjK7psP1AwXBynv37jE2NpZWqzeBzcqzGIpiXX777XeMiYmhh0cABeFbAncIrKco+jgt/T/8cDj1eokmUzANBis/+2wC+/cfRJOpLYH7BJIIjCAQmG4lBFQkEErAg0Zjbc6cOZMkuWTJUoaEhNPLK5A9evRhdHT0I9/B/fv389SPOz8zb+5clilcmF4WC7u2bctr1yS2x4AAACAASURBVK49NG2bRo04z/kLZUWrlTt27HBKN/HLL1lLFHkB8kneIQAjAJ4G6G42ZzioPy12u53d2rdnaUniMEFgLYuFNStUyDb1ZlZRBX8+Z8qUKTSbOzsJMoulJefOncu4uDjOmzePgwcP48qVK5mcnMytW7fSxSUsnQAOCQnLduuTuLg4LliwgOPHj+fs2bMpCEXTDDifUadzo81m47Zt2+jiUjVNu5axVq2XuWzZMlqtzhZNGs0o9u37Pkl5c7dYsVDqdBUoCJ2o07mxZcvWHDNmDBs0aEGDwYWCYCEQQqAzgQlp6qlCYAiBb+ji4sObN29y3bp1FMVCBNYSOEGD4U1Wq9aANpvtkbN5u93O/fv3c+/eveqsP5MkJydz8+bNXLBgwSMF/MNYvHgxS0sSzwC0AZwJsIiPT7rzEzabjUPff59Wo5E6gMUFgQMABogip07OXrPov/76i0GiyFiHVUgjSeKsPBKGUhX8+ZyRI0dRoxnsJMiMxnf41VdfZZg+MTGRPj5FCHyrzIKPEShKo7EM3313cLa169atWyxatCwtlgbU69+lyeRPq9WfwFuUY9jOI2DhkCEfkUyxWipA2TpG7ocgTGCHDt35ww8/UKOpnUbwj2Xv3u+RJHv37kuglTKo/E3Ai0AH6vV9aTZ7cd68eZwzZw4BiUA45T2GP5SVwGRqtS40mVxZrVojHjhwgCRZr15LpY12yvsEhQhYKAh6GgwSe/bsn87H/Pnz51m8uBwgxmIJYWBgmSz7HXreiY6OZljp0qxgtbK11Uo3k4lLlzzZnpPdbucXY8bQQxQp6fUMK1WKR44ceWj6pKQknjlzhl9+/jmHDhrEyMjIrHYjHTNmzGAPUXRahUwCMn0iOKdRBX8+Z//+/Yr+POVg1zGazV6P1FceO3ZMcSWspxxKcCqBizSZXLLllGlCQgI7d+5Gvb4JH1i7XKHR6MqGDZvTbPalp2cxTpw4MTXPvXv3WL9+C5rNLQj8TkH4hqLoxQMHDvCll9pQdnw2RxmsdlKj8eCePXtIkh4ewQSWKvU04YPQjCSwg97eRZiUlEQ3Nz8CpZWZfyECWnp5FWWtWo1Zv35rLl0qO1RLTExkqVLVCKwkMIvymYTmBLoSiCZwmWbzyxwwYIhTvxs1eoVa7QhlsLBTo/mMtWq9lOX3mRfYsmULX23ShI2rVuXM77/P1GomISGBX372GWuWLctWDRpw69at6dIM/eADdjMYUs089wP0lKSnOtWbmJj4WHXcsyIyMpKBosgYhw3qhpLE2bNn53bTSKqC/7ng66+/pdnsTqs1hKLowR9/fPx/LlmfftJBMMdQqzVkybLh5s2bbN68LQVBIlBKucoQOE+AdHVtmM7z5bFjxxgaWp06nYmi6MGGDV9mhQp12apVx1SLH9mOfiuB6pQtcYpSELSpm33u7oUI9FH64ZtaX8plMFjZtGlLyjb2sxVh7svAwJKKp81ZlO34Q9i37wB6eATQaAxQ2h5OeUPaROczAqfo5lbAqS96vZnypvFuZYCKpSBo8r3KZ82aNfQXRf4I8FeAVUWRH/Tt+9h8ndu0YROzmRsBzgboazZzy5YtTmlqli3LLWn08yEWC7t06MDXmzfnnDlz8uUBOLvdzh6vv84QSeJgjYY1LBbWrlRJ1fFnx6UK/gfcu3ePR44cyfRMqVu33jQYulA+EJZIne5D1qvXIl06u93OiRO/pq9vUUqSJzt37pnO101KuooVaxMoTMdg6MDHBDoQuEGTycPJTDMpKYm+vkEUhGmKoPyHkhTCZcuWOZXt7h5AOTA7KZ9LOE4XF9/U5z179iXgTtmMswyB7xzkyB+KdY+VwH6H+5HUaFwUlU/KvRMUBFFZPSRTPoXsSmAdZTPQmw5pj9LT84Fb8OjoaGUVFaCsEIoQWEw3N/9MfR95mboVK3K5g2C+BtDFaHQ6fZ2Wixcv0t1oTJ3xEuBcgC0iIpzSdWvXjp9rNKlpIgGKAAfrdJwDsLok8Y327XO6izmC3W7npk2bOG7cOC5dujRPbfyrgv8F5e7du2zSpA2NRlcaje4MD6/Py5cvpz6/fv06+/Z9nz4+gdRqSypC8z8aDN3ZsGGrdOUdPXqUZnNByqdnbQ4C8iIBK0UxiAMHDnPKI1sYVVDS3VFm1J/Tz68EK1euz/btX+fgwUPYtWsPimIogd8IrKEoluenn45PLefAgQP09i5IjcZKrdaHgJkGw+s0GHrTaHRl6dLlCWjo7EP/LgEtneP/7lRWFN9QNguto6xaKlI+F/AK5UDxJyiKdThixJjUNrRq1V4ZeFL6/hMFwY2TJk3JgW/v2VKyQAEechDgdoDeJpPT4UG73e6kJjxy5AiLWSxOM/ktAKuVKuVU9vHjx+ljtfIDvZ5TAPrpdPxEEFLzxAD0MZv577//PrP+vgiogv8F5/r1604Cn5Rn4sHB5anXv03ZzHGjw+83jkajG69eveqUZ8+ePbRYSlE+QOUYhvB3ursXSWdal5JHkoIJtKfs4MyizLA/omxN04xACZrNlViyZDkGBpaip2cRdurUmXfv3uX169fZtWt3arVWarXvUqMZRIPBgzNmfM8pU6awVKnKNJkKUK+vRsCFwKdM0b8Do6nReBD4QPk8gbKa6ENlheKrzNyTCIxWVhQmajQiXV39OWzYyFQVxN69e5V2H3bot516vUe6U8D5kQHvvMOuBgOjAU4EWBOgv7t7anDx6VOn0t/NjVpBYESVKjx58iRtNhuDfH25UHkhsQBfNpv56Zgx6cqPioriRx9+yLc6dmTVkBCuSKP6qenikk5F9CTs2rWLNUJDKer1rBEayl27dj11Wc8LquBXScf69etptYYpArEige0Ov8NEGo0evHjxolMem81Gf/9iitAMJvADgW9oMPhw2bLlGdZjt9tptRYg0JGyhU0MZd/5KeapNgLFCeyiVutPk6kcgS8oiq1ZuHAIfX0DKQgFCSxzaN9WGo3e7N+/P0UxgvLBLCoDScreQzAFwUqDob7Sv6KUD3Y57g18SKC8kyAHRnPQoCHp+tG2bVelXMd2XKHB4JIj8YRzkqioKHZs3ZpFfXzYrE4d7t27l7dv32aDatXoKghsCnAewM4GA0MKF+aSJUtYTJJ4CLJfnW8EgUX9/Hjt2jWOGzeO/q6uLG6x0NNk4mstWz52D2n8F1+whdlMmzLbXwnQzWTinTt3nqo/Fy9epJckcT7AO5DdRHhJ0lP5hHqeUAW/Sjp++eUXWixtFAH2FYGaBP4jEEOdbhDDw+tnmO/YsWMsVSqMOp2ZGo0bS5askuGRfEeMRivlE8QpAvMWZXVRir1/E8oHrvzp6OhNr29Dna6SIsyjHfLLjtnkGbpzWEWNpiotFg82atScISFVCWxS6vlJmd07TjS3EvDkA71+DC2W0HTuHEiydu1myirFn/L+xhICZdi2bads+T6eFbGxsSzi48NRGg1PQraH95IkRkVF8dWWLVlOUfOkvKRWosgqoaH8Mc0MPdRsptVoZCurlbUtFhby9s60yWRsbCyb1KpFX72eEkAfgO4mExf88stT9Wny5MnsYTI5ta+HycTJ2Wy3n994mODPrdCLKtnEsWPHEBkZCZvN9sR5GzZsCJttC4C9AN4DUAVACWg0bqhb9x+sWjU/Na3NZsOCBQvQq1d/bN68BZGRm3HlynnExFzGyZN7UadOnUfWZTSKAO463LkDwKz8vR9AJACj0oYHgdmTkhrDZjMDCAew1CH/CgD+ALwAHHS4nwSN5izWrVuJDRt+Q0hIMAThKORge60A/AfgjEP6JQgODoDZXAqS1A56fQD0+vuYNu0n/Pnnn0596NSpJURxG4AfAGwB8BXM5vOYN++HR/Y9r7F69WqUjIvDSLsdfgAIoER8PAZ/8AE2rVuHWpDfVgoVYmNx/r//nEIcEkBMfDxGJyRgxb172H7/PrreuoVvx2cuBKXZbMa4yZMBjQYHIIfk2xwfj75vvomoqKgn7lNycjJ0pNM9nd2O5OTkJy7rhSCj0SCvXeqMPz3R0dEMC4ugKBai1VqWvr5BPHz48BOXs2zZclqt3rRaS9FolP3PpF2m2+12Nm7cmpJUncAkiuKrDAoq80hrj7R8/PFoimINApEE9lKrrU6t1oVabRkCZmo0LWg2B1OjceMD18g2Go31aDAUIvAnZX38KwRaKCqbtwl8QtkSZzCBhQTqUxBKMDS0Gu12Ow8cOKC4ZB5L4P8oW+G4EehL4GUCsrro5MmTDA+PoMlUj8AaAtNpNnuzSZMmbNCgAefMmcM///yT7u6FCZgpCAVoNnty/fr1T/zOc5tZs2axgyQxCmBBgO0A9gPoKggsLQj0Vyx6CPAuwBIAa2q19NBo+AfAGwBHarW0CgJtDjPskwCDvL25ZcsWrlix4rHqrxEff8xhDpY+hwCGajSsVrYsFy9e/ETumc+dO0dPUeRqyLb0qwF6iuIL6YrZEaiqnueLN9/sS4PhTT6wLpnF4OCHO7h6FHFxcTx06FCqs7W07Ny5k5JUgo6nbc3mV/nNN5m3ZElOTubnn09gwYKlWKBASY4Z8xnPnDnD7du389dff+UXX3zBDRs2sEOHrtTrvanXd6deX4re3kEsX74a5VO4EQQMlDdYf3ZY1a+lvGHcivIhtXhaLCVSD34dO3aMPXr0oV7vTXkT+4iiVppHoBa/+eYbXrx4UYkQdt+h3B8oO3YbTKAAtVqrUn5pyvsb4RRFjyw7vnvWXLlyhe5mM5sDHJFGcJsABgO0AHwJoK8yKNwH6KLTsXiBArQYjWzVsCHdzGb+o6iFtgNsDdBdr2dFq5WNXFzoZbE88t18NXFiqnrmT4DeAMcAnAUwVJL48aBBT9SvDRs2sGxgIAWAZYoU4YYNG7L6qvI9quB/zvD1LUZnN8PJNBo90lnuZAc//vgjRbFbGt341+zevU+21WG329m+fTdKUnFqtY0VAd+KwOcUBD9llr+YsuWRmbLXzwd9lw9e3Um95+JSgxs3bkwtf/78hTSZ3CnvFfSgfFirB7VaF/7000/866+/KIrF0/RxM4Eayt93lMGnPYF3HdKsoa9v0Xx3eGvt2rX01GgYmUZvXxJgA4CeAMcCPKXcTwboZzY7uaaYMmkSC5vNrAqwCMBQgH0cyloDsKiv70PfzZUrV+jr4sLxgsB6isBPyXsFoKvJlOFZksehxj5+QJYEP2RlbMnMpM2JSxX86QkNrUlghcNv9jJNJtccCWwt2+778cEGaCIlqSb/7//+L9vq2Lp1q7KqiKXsR3+KQ9/OKeqZOwSWU7YAGu7w/GfKm7yXKG/i/kpXV9/U05P//PMPzWYvyn78rxLorqR3pSD0pdlcmRqNlfLhr5R3Gk/gJQLjHOppSnlj19mc02z2z5NBuy9dusRt27bx5s2bGT7v3qEDRzjY0p+C7L54HUBfg4F19HrehOwQbbxGk6Hv+6+//ppFdDrGKiqhIw7C2w7Q32x+5Ls5fvw4X2vRgp5aLfelGYQKS9IjY/U+Dffv32f/nj1lKyR/f06ZNClHQ5XmNk8t+AG0AHAKQJTyuQKAVY/Ll52XKvjT89tvv1EUCyjqiKUUxcrpDk5lJwMHDqPZ7EdRfIMWSynWq9f8iU8o2u12fvPNtyxYsBS9vIrw3Xc/TA2yPX78eOp0A5TffFnKTtgc5UBhyuaf5Snr9b0om1aGUfZD1JaAmTqdF/39g/nnn3+m1vvJJ59QECpSVs/UUmbyvQiMUQaKUgTmUnbBEEA5iIwLZdfOKSEwbyv3yihpU9r1H81mt2x19ZtV7HY7Px40iO4mE2u4utLNZOJ333yTLt2ZM2cY4O7OZgDfg2xZ8z3AbwG2bdqU77zxBq0GA92MRlYPDc3wcNWECRP4nl5PAmwC8CeHL+0/gG5mc7rJSHJyMufMmcM2jRqxV5cuPHToEN/r3Zs9HUI2rgNYxNs72904dGjenK8ZjTwNcA/AcqLIGdOmZWsdeYmsCP79AFwBHHC4d+Rx+bLzUgV/xmzZsoVNm7ZjzZpNOXv27ByfuRw5coTff/89//jjj6eqa+rUaRTF8oqAPUaTqTXbt3+DCQkJXLBgAU2m4gTeoWxzP8BBuO6krN5xo3wSl5QPXL2izMD3KLPwVhQEDw4fPipVENvtdvr5FaN8huB9yj55dJRt+icTOEPZt89yyv59tiv/WigIVgJVCQwk4Mvw8NoMDCxNeTN5JIFplKSSHDny0+x+1Vliy5YtDJYkXlde4BmAXiZThrPn6Oho1qtViwF6PT8D+JlGQy9R5L59+0jKJ78zCm+Ywrp161jeYmESwJ2Knn4kwO8ABun1LOTmxgZhYU7msf3efJPhksSfAX6u1Ldx40aGlynD0hYLI5T9gYycvWWFW7du0Wow8L7D4LQFYKXg4GytJy+RFcH/l/Kvo+A/nIl8swFcA3A0zf3+AE4COAZg/OPKoSr4nxsCA0Mph2VM+d3dplZrptnsRq3WrKhfPiUwiIBEQahASWpBg8HCtm3bUq8PT7MK+FVZHVgp2+I3JLCQJlNbVqvWgDdv3mSjRi0p6/9dlFVDEco++WtQ3qQdR9l7qYVACQJBylWNZnNpNmjQgE2bNuW8efNSZ5+HDx9mr1792bZtN65atSrPqQoGDxzIMWnUJm+Zzfz2229T05w/f56vvvQSXUwmhgQE8O1evfhq48bs3a3bE1mHJScn8+W6dVlVkjgOYHmdjp4GAwu7uzNcr+efAJdADqm4du1aXr16lW5GI6Md2vYdwHZNm9Jut/PPP//kmjVrHrmCmvHddwz09qZRp2PrRo0eGo86LdeuXaOrwcAEh7r/AlimcOHHZ86nZEXwzwLQEcBhAMUBTAUwIxP56gCo5Cj4AdQDsAmAUfns87hyqAr+5wY/v2ACBxzkUTzlQ1x/K2qV3ZR9BU0j8A2tVm/q9VZarXVoNHpSq5UI/I8punV5L6Aw5Q3XZZQ9dxYlcJOiWJTlytWgTteT8kbwYsrWQHcc8rdQ6i/MBw7f7MrAU5/Aj6xfvzkrV65LjUZHnc7KiIjGed6fzORJk9jFbE4VbnaAdazWVKd4ycnJLFesGIdrtbwG2SKnsChyw4YNtNvt/HLcOPq7udGs17Nj69a8ceMGExMTuW7dOi5ZsiTdhmtSUhInTJhAq17PxlotRwOUgNTgJAS4EGCT6tV58OBBhlitToPSNoBVQ0Iy1bfly5czWBS5D+BtgMO1WlYsUSLTg2/DatU4SNmTuAywntnMzzJwL/G8kBXBLwL4FPIpn70AxgEwPS6fkjcwjeBfDKBhZvI6Xqrgfz4YMmQEzeamlO304ygIAwhUoKy2gTJ7NymzcR9lFp7irfM+DYZAarUWAq2VfCUp6+wdo321V4R4qDLLTzFBXaQIekeZM0eZ9RuVNqTcv6jUPVox8QxVBo1+BPrSYHDl999//1QWJ8+CGzdusJCXFwfpdFwLsLvBwNCiRVMDykRGRrKUxeJ0OncmwA7NmnH6d9+xkijyOGR7/T56PSPCwliyUCFWt1r5shJEpU2LFvxqwgSePHmSzerWpVEQaALYE+BhgIXhfPp3K8DwkiWZmJhIfzc3blLuJwFsYTBw0HvvZapvLSIinAK02wEWk6TUwDqP48qVK4yoWpV6QaBJq2X3Tp2eayugpxL8ALQAtj4qzWPypxX8BwGMhnxM8w8AYY/I2wvAPgD7Cj/HS7H8yq1bt7hw4UKuWbPmkZu8hw4d4oQJE/jLL7/w9u3biptoC/V6kV5exQiMVX7DBSibWqYETU8kUJvO1j3fsVGj5tTrJcqRxYZQttBxFOYjCXRRhL4r5T2DGpQ3ht0ou4pImdk3pbyZ6045ZkFKGRsJuFOrNSltepuynt+L8irDhYIgD1Ki6Mn33hucZ/yvp3D+/Hm+17s3G4WF8ePBg50se3bt2sVyaWbdcwG+2qQJw0NCUoUyIfvlcdVoOESrTb23GqAbwEIA3QSB72q1jAV4E2BzgEMBlgI4XRHMdwE2Npv5+bhxJMlNmzbRU5JYUZLoKgh0EwS6Go38sH//x5rFNq9Th/PTCP5gi4V///13pt7LqlWr6GU2c6BOx3f1enqJYpYcw+V1sjLj3wzA9XHpHpI3reA/qqiKBMhn8KMACI8rR53x5y02btxIUfSg1dqCLi41WKhQyXTO3Ejy888n0mz2o8HQnxZLYxYuHMLr168zNjaWd+7c4a5duyiKfpQjYFkI1EsjxBcqs3v5s8HQjx9/PJJLliylq6sf5U1WT2WGTkWoB1A+AzBRef4BgW3Kv5KS/n0CdSmvGnyUAaII5UAt3ylltKRe70lZRZTSns+UdCmDxykCbjQaG7Jr17ef+ffwOCIjI9m6aVOWL16cXV57jSdOnCApO9oLLlCAkwSBCQCPAiwuilyxYgXDSpbkZocvIRHyYa6TaYStG2RHaCaAcQ7PjgAMgux0zSoILGA209VoZPcOHZxCWEZHR9PPxYW/KOVdAxgmio+NXLV06VKWEEUegnyobJRWy/LBwZlS9djtdpYMCHAa2JYBDE/jQvp5IiuCfyVkByezAExJuR6XjxkL/vUA6jl8/heA9+PKUQV/3sFms9HbuwgdXTjrdB+yY8c3ndJdu3aNJpMbHT1hGgw9OWiQs8npmjVrWLZsDUXw+/GBl00qwro4ZYubgXRx8U0dYP766y9KUkkC45WZfANl5h5I4E0Cr1J2yeA4kDRQhP3nlFU/CZSdrrlQNuGMoGxR9KHy2UDnoCx9mT6Ae0fK3kmlLEU1y24W/PILvXQ6jgI4HKA75KAqKaeZT506xXphYdQKAn1dXFKDkH83dSqriCJPQdah9wfoD9lTZ0qnTyvlDVX+bQV5k5QAdwP0EQS6mUycN3cuT58+zevXr5OU3Sq80707a5QuzVdbtmT5NH78FwN8uVath/bJbrdz37597N+nDwPc3anVaNg8IiJDtwz379/nqlWruHnz5tRN+aSkJAqQD6Ol1HkboKjXZ/frzzNkRfB3y+h6XD5mLPh7Axij/F0CwHl1xp+/OHv2rHJ+wPE3e4T+/iWc0m3dupWurrXSpFvNqlUbZ1jujBk/UKPxpqyWmafMykXKQVQCKQihfPvtfiRl+/NevfpQp/Oh7LLiMoH1yiw/jLKVj0DgvTT196Ecg9fx3quU9wksirAfRXk1YKRs5fODQ9pOlOPxpny2K/Uto05nTj2TkFusX7+eL9WsyfCSJelhNnO7Q0dXAywGsFWDBk55EhMTnWbLdrud7/frRxNAPcAOAH9TBPxHAL8CWABgeYCvQDaH/A6gF2R1UTmjkd06d053gjw6OpqFvLw4VKvlNoDdtFp6A06+fqYBfL1lywz7Fh0dzZoVKjBYkljDxYU+Li787bffOOCdd1g5OJjtX345Vc+/c+dOelutbODiwspWK8sEBaW6Z64QHMxlDnXOAli3UqXs/BryFE8t+J/2ArAAwGUASQAuAHgTgAHAz4rK528A9TNTlir48w6xsbE0m90p27//R1n37UdAz5Ilq3Ds2LFs06YLO3ToRoPBlfJJWfl/ml7fj2FhtTlw4GDu3buXpCx4xoz5jEFB5RkQEExXV39l5i5RDo14T5nVF2Dz5q8xMjKSkuRFvX6gIqg7UnZd8RtltY0n5Q3hSMrBYs4q9Z+hILjR3d2fsr5/FeXN2kDKqh4vPojcZae8MkjZZG6nCH1RGSCGUvYP1J5Aaer1vdi0adtc/V7Wr1/PAqLI+ZA3UusA7OIg4G5BtrQJLVLkkeWcO3eOVcuUoRGgFWBfRd0zBWBRHx+GlSlDvbIKSHQo/yuAPiYTJ0+cyNWrV/PVli1ZumBBRlSqxNWrV/O7775je1F0muEHaDTsrNPxAMBFkGP1ZhTIhyTf79OH3Q2G1Nn6SoCuWi176XTcDfAbyK6l//nnH5YICHAK8jJIp2P3114jSW7fvp2eksTXRZHtJIneVmvqmYXHsWnTJvbp0YPDBg3i6dOnn+j7yS2yMuOPguzH1ul6XL7svFTBn7f4/POJNJn8KKtIuiiC1IvAl4rAHkON5nPqdG40GgMoCMNoMrUmIFGn60VB+ISi6M9Zs+awR4++NJsbUA5evo5abRHKenTHeLokEMJBgwaxVq2mlH3hd1YEexE+iOjlSmCmQ56qirAuT8CVOl0I33vvQ5YqVYmy351iynMDNZpWaeqbTNkbaFsCb1KjSTm4dZiyyqch5RgCVlaoUDVVnZFbNKlenQscOnAfoAtknzdxyiDgCbBdq/ThNB2pUa4cR2m1TIRs1dMA4EDIJ1znzp1Lkuzzzjss6fyyuAxgw7AwlitWjH5aLVsB3Kvc99XradLp+EGaPG8ArFS2LEsFBLBBWBh///331Hak1dmXLVzYyaWDXVll/M/h3hCdju+89Ra9TCYni6IUr6EpXLt2jd9//z1//PHHh7qzSMuEzz5jkChyAsDBej29JCnTA0ZukhXB7+lwBQAYkKKueVaXKvjzFna7nd7eQcqsN+X3NY9y7NoBlEMYksB8hoRU5ogRnzAwMITAdIf0B+ni4qsEaLnhcH+LInCnOQlirbY0N2zYQC+vQMqHrmrxgTuFw4pQ9qZs7UPKewVmyjP+vZRdLjwInN6373vU64OVQeQPZSBICfSSrAj1ryhbBbmzb9+U1YGj7NpMIISSVCnXN3erFC/upNpJEYzlIfvfqQ7Zy2aA2czRH3+cYRnnz5+nl8nkpH7ZBdly54uxY1OF8dy5c2nRaLhOSXMXYG1RZO2qVVNVOI6HpOYBrAJZRXROufev0i43szk1bKXdbudXX3xBfzc36jQavtKoEf/55x9OnTKFxX18nNxBXIUcrP22w70pAN/o0IHuosgoh/vzATYID3/qd3v//n26m80861DmDKRXm+VFslXVA2D/0+R72ksV/M8eu93OGTNmsnjxyixcuCxHPAa+7QAAIABJREFUjRqXarYZHx9PQdDS2X7+FmXd+geUfemQgKyWadToFWq1LsrM+3XKqhg79XqrcmJ3KWXLnljK5pzulGfykcq98fT1DWJSUhJbtepIoBLlaFqOQriBYuPvrQjk85T19Lcd0jwQ/AUKlCSwz+FZfyVvL8rnCapQtgYqyQoVqjAyMpJ6vYsySJDy3kI7yuaj92gyeTl5rnzWjB05ks1MJsYoQn82wJIFC/KVFi34uoMp5lXIka4yihF8/fp1Wg0GdgQYCLAR5Ni7NcqUIUnGxMRwwhdfMNho5DjIvn0KA7RqtXyrY0eGFCjAJZD3Ehxn3OsB1gY4CbI1UDBktZMLwFf1ek6YMIEk+dPcuSwrijymDCaDBIGugsDmej3fU9J/qQwk/8/eeYdJUWxt/HRP6u4JmxPLknMOS5YgIEgGRZIkASOgSDBgwiyggigYSFflmhAEMWJGRQwkxQACgggqIChK3GV+3x9VszuzLIJk78d5nn6Wmamq7ulm3jp16j3vqeE4hFwuPtbn+EWECn4/L7/8MuPuvpuyfj+TRbjLNElxnOOSf1i3bh1ZBcJUK0WomJl5zGOeKjsej79W1JGtN2hXHqnfiTzOAv+pt0mTJuM4VTWILsG2W3D55SrJJhwOU7RoBUTejPotzNLAnoAqwv4nKt5eFBX+GYgqpvKABtjrMQy/9tRrojz4DDyeliQkFMXnK45hJCBiUK5cbVavXs3SpUsZPXo0Hk8cKrs2cu4cRDJp164LrVq1RYV8vKhQVFfUPsMGbLsFN9xwKwCZmRVQGj+RMf7ENH14POUR6YhadWRhmtm0bdsR207HNC/Q36UGKtu3hf6eEBfXhLfffvu0PKtwOMwt119PnGniiJBmmpTOyODLL7+kU7NmzI6dIWkZCvHaa68dMs7+/ftJsixuEKXU+ZQG6NvHjKFrmzZYLhdBUdr5iIrxvy5C0LLIycmhbePGTBWl0vmEBv+dooq2T9B9dopQVve7W4SahsHdmt/fpEYNBorQT7f/XdSqJUIl/VSEDJeLprVqMWvWLObOnUu841DEsgj5fNx6/fV5q5JXX32V/hddxJBBg1ixYsVx3d+cnByKJibyftQ9HOl2M+jiM7/k5vEA/3tRx1ui6s6dUonms8B/6k0B+ydRePErPl8wj7L4+uuvY9tJuN2XYxi9EbGIj8/AtkP4/dm4XHGoLNxhKGG06NXBUO2N19MTQeT9x7GsVEaMGMVtt93GM888kxc7v/76W3GcTLzeIdh2DT1h3IhaLbTA7U5g/fr1ZGVVQG2+xqE2npsj4sHlchg27Hr++OMPrrhiGD5fvJ4YhiHyEz5fX84//0KSkopimnehNozvxrbj9Cb1xrwJQqQyptmE/CI432FZ8YctZHOybdrUqdRyHDaIsEGEYYZB9TJlWL58OX169mSAz5cHWNtFSLQsfvzxx0PGeeWVV2hUILHrRtOkWsmSDNLiZin6HJHP94ngc7nYs2cPixcvJtlxuNI0SRIhTlQ4Jk57/b+KMEZUcleuCDNFSDRN1qxZw759+0ixLDqIME2EC0SopYF/TdT5LrcsJk2axJo1ayiRlkb9YJAGfj9Fk5L4+uuvj/kezpkzhw7NmtGhWTPmzp17yOevv/46CbZNl2CQRsEg5bOy/hWF3E85q+dEHmeB/9RbQkImImuiMGAvbrcdU07vhx9+YNy4cTz44IN5QHLNNdfh86VhGH1RkgqVUCqa0XjyIC5XGQ3e0aGYHBQNsz1+fz0aNGjBd999x7p167CsJES26XZhvN5WlC9fnSJFKtG2bQdWrFhBdnZjDfhpxOro78Kykti4cSPdu/fH5aqI2titiUhJXC6Hiy8exEMPPUJKSilM08blSsAwHD3J2Ki9gMh4r+ByJeDzlSMQuADLimfatJmn5TkBNK9dm5ejbnBYe/0ZlkW9QICAYdDWsrjBMCjhOIetbPX888/TXgN/WIRtItwjgscw+EOPPUiEgdrbPygqR6BVw4bk5uayYsUK3nzzTS6+6CKSTZNvtYf/oqhKXj4RmoqK82/TK4MRI0YA8Oyzz3KOz5cXIgrrlUJQTy6R1UJRx2Hp0qW0adyY+6NqCUw2DJoeI05MmTSJso7Df0X4r6hktkejBO0itn37dv773//yyiuv/GNJ8tNlx+PxXyMiIZ1tO03TMFsdqd+JPM4C/6m3yy+/Bp+vO6oU4QHc7utp3LjN3/b57rvvsKwUomvmqg3fEPlhlZ9Rm6RlURo4b0UB6hJUaCgRkf6I2Ph8GSQkFMVxIoVnJiKyEpGnadeuR96527XrhsvVDaW0WZTYjWdwnJq8++67Wmq5K0os7jVEihEXl8rcuXNxnNL6Gn5FVdnKRq1U5qGSxCxEaqMqg6UjUgmfLxAjOXw6rHWDBrwQ9WVzRTF4IiGS10XwGQYZiYn06dnzsLVwd+7cSZLfz3gRKoiKqTsi2KbJL1Hge46oEFCyCIluN2+++SalMzIoFwiQYlnUq1KFzgWSs6ZpIM/Shy3CyKFD+e2337i0d28SLYuiIsyK6jNKX0cJvQJIt21GDlG5HJbbnTcZIWoz2TCMf1QJbdmyZYy+7jpCXi8NRagswrUifCBCseTkE/JsTrcdD/Cv1H9bi8hLIlJZRJYdqd+JPM4C/6mzNWvW8Mwzz/DJJ5/QoUMPvN4QPl88des2P+LS9umnnyYQ6F7Au38MlRGbqAE5hKqe5aB48GkoKeb7UHo9T6P487VRWbNhRGbr9nVRLJsMXK7a3H67ig3n5uaSr9dfX/+7DCKvo9g9X+D1hvjqq69QMfq9Udf3Ii5XEo0bt0Pk2aj3D6Li+B+juPwzULUA5ukxIvLSz1OyZNXTKs08Z84cSjsOH4oqfnKpDpNEP4gSouLm3X0+mml9/FWrVh0y1iuvvIJtGMzTXvc3GuCLGAbLRCVpJYqScQiIUKlkSSoVL85M3f4pEUobBiFRG8x79cqgtcdDnG1jiNCoRo08bZ0W9epxmdfLOlHJYCVFmCfCH6JYQHVtG7/Xy4BLLuGrr77Ku86KWVkxMfdPRRVuOZzt27ePN954g3feeYecnBz+M2MG6Y5DX8MgKIr5s1SEvqJyINymeeIf1Gmw4wH+L/Xfh0Ski/738iP1O5HHWeA/NTZy5E1YVgrBYFccJ5OePQewbdu2wxbi2Lt3LzfccAtZWZWoXLkBd911N45TPAZYDaMzIiaKpfMQiv3zHY6TQIcO3XWsPSJ+9pEG+kxU7D4au8pHge0viPjzdOOHDLkaJce8C1V+sTJqVVEVxTTycs45zRg7diyGkYRiCl2L2mROwedLoUGD1nqCiZwvrMe8HbVCiL6Wa1AZvmqC8Hj87Ny581Q+qkNs5vTpVMrKIiUQICs+nruiLniXDpkM16uBZBHq+/1kOg7d2sdWUnvhhRdoq8M9OaJi8330xOHXnvoLGuTXanC29evbRKgmwtOiEqpC+rPSXi/n1KzJ7t278zzybdu2MXHiRNJ8vhj66LMilDRN0iyL7p06MWfOHLZu3XrI933hueco4jg8YBhMFCHLcXhy5sxC782XX35J0aQkGoVCZAeDlMvMJDkQYIUIF4vwcNT5c0UlpzWrV++kPKdTbccD/DNFZKGIfC9Kojl4ls75v2fLly/HtouQH6bZjd9flQULFhy2z4UX9sGyOqB48q/hOKWoU6cpfn9NRO7DcTpTokQlrrxysAbgOxGZhMdTlAcfnAQoAEhKKorbPQKRVxDpjmIDPRIFtLmoSlur8t7z+8/PSyhS8f+bUaJpfVCMn8hm8iw9kSRiGBnkC7J1RGUff4fH04gOHbrgOJX1GHs1sGeiQj4F5ZyvJb/m79f4/clnhLTvunXrqFyiBBluN44Id2lPtoEoauYFekWQLSq790HttftdLtq3bMm2bdtYsGABjQIBfhJVeL2+CM30xGG53YesJKbozzaK2sT9Keqz2SLUEyHZ62Xp0qV51zl3zhziLYumtk26xFI/XxYhwTRJ8HrJiI9n5vTp3HTdddQpV44u553He++9xyeffMLUqVMZPHgwF7Rrx8CePXn//fcPe18aVKnC1Khz3GGaxOn9gfaisoajv1M5w8irXfBvt+MBflNTOeP160QRqXakfifyOAv8J9e2bNlCv3798Hg6Ecu+uYtrrx1VaJ/t27fj9YaI0BnV8QpVqjRk7ty5DB06nMmTp7By5UqtaR8plNIDny+Vt956i0GDhmBZIWw7nipV6lKnTkt69uzDoEGD8PlSNGh/hsvVHcMoGXWeXYiE8HiC9O17GflSClmoUFI0zfSgnnQStEe/HMUo2hrV5hvi44twzz3jCQZTMAwXphmHiuVX0OO/oCegt/V47YlQUydMePgUP7HCrWX9+txnmoRFaeLXFsWZnypCDe2Bp2iwHyQqfj9YhFv1KiDZcRjYrx9+3ff6KDCcr733sgVA8l49XrYoPZ9oEP9aFKd/uGly+5gxbN++ncv69iXOMGggwtsiVBXFzT8gwiYRKokSf5sjai8hQYQqpskiES4TpQYaZxjUE+EK0yTdthl39+FLX+7duxePaZITdV2/6nHeFRWOyha12RzWk0BmYuK/ZvP2SHY8wN9IRPz6371F5EERKX6kfify+LcA/9q1a3njjTdOe/r+P7Fp02ZiWfHYdgftCZ9LRLPGcTrx6KOPFdrvxx9/xLKSNbBGflOfULx41Zh2kydPxrYHxHjMhjGGSpWysawLUNWxNmLb53PNNdfl9Xv33Xdp2PB8SpSoxmWXDaFIkTI4ThdU6KUoqjxiRKrhfFT4JozIw6j4fmQCW4eKyU/Qrw+gNml3Rl3TWkKhNEBx4levXo3fn6XbtNbgn4WIoEJOF2AYyZhmiDvvvPNkPZp/ZPv378dtmnkMGEQlNTkilBK12RuhRb6rJ4FHo9p+q9+7RvKTq6JplGFRMssBEUaKCvPM1uMWFaXVkySSp5UfFuFKEa4QoZsIXS+4gLqVK3O5282Xul2qCHNFqCmCVwS/201d02SanrBe0BNOFT0JxYvav7gk6rp+EiHesg4bjjx48CAZ8fF8GdXnTRHKZWSQaNtcbNtU8HiwRMiwLMplZuYpmP4v2HHF+DWjp7qILBeRwSLywZH6ncjjTAf+gwcP0rfv5VhWCnFxzbGseCZPLhwwC7Ndu3YxefIUhg0byYIFC/4RM+F47LffftPSyZEiJAc1iHbFsnpSokSlQhkgOTk5PP744yQnF8EwRmhP+A9suy233HJ7TNsXXniBYLB5DPD7fP11kZNor3stweDhN+d27drFtddei8eTrr33/6L2C0qhMnyjPfwQSm55qp4EiiLyTFSbvqiQ0g4Ug6clLVu2o3r1xlSoUI/773+AYDBV9+2OqtTVGaXEuQfLqsaQIUPOKB73wYMHSQ4EYnTzPxYhzXFo2qABd0VRHxHl4X8T/VBExbb7aYBNE4mpdPW9CEl+P13atSOo2xTVnvMbus0dIvgNg/KiMnpriIr7J4jguN2ULqChc78oauh9omQhbr/9dno4DhVEFW6PtFsvKpzUWlToaX6B624aF8dbb7112Hvz+JQplHAcHhHhAcMgw3F4ae5ctmzZwl133cVtt93GDz/8wPr16w/72/vPjBlklytHxaJFufXGG88oCe6/s+MB/mX6760iMjD6vVN1nOnAP3v2bPz+Gijqo/IyLSuhUJ3wgvbbb7+RlVUex7kAkbvx+6vQu/elp+CqYeHChcTFNSP2dzSH5OSyjB07vtANy9WrV+PzJaMki7sjEsLlCuLzhejRY0BMsQ1QS+2srPJ4PIMReR/TvI24uHQN/NuizrueQODvKXRffvklHk88Kgs40q8pquh65PXvKFZPFQwjgbi4IpQvXwVFH92iVwLTUCwjnz4cbLscSuHzCdzuLOLiElBspMi4YUSysaxSnHtuuzyN9zPJ7r/3Xir5/TwnaoO1uG0zcvhwBvTty9WmGQOWRUS4Jer1Iu2BVxXFqPlAe/A3izBRhJKOw4Rx4zh48CDTp0+nYe3anNu4MfWrVaOI41A5GKR4aiqffvopQcuiuag9grJ6Qqmij+hr+I8+X4oIAY+HNWvWkB4XR0hU2CfSbq/kJ4KV015/5LNtcngJimhbuHAh/bp2ZVCvXixevJj9+/fTo2NHUiyL6qEQ6XFxh90nmDFtGuUdh7dF+EKEdrbNwF69TsYjPOF2PMD/gYjcqDd303XM/6sj9TuRx5kO/H37Xo5irOT/vw4EuvPkk08ese+dd96DZUVrvP+FbWcUSrU70bZ27VpsOzVqwgK3+3r69BnIHXfcxahRNx6iQFiyZEVU+CMS4tmCaQZiqHYF7ZdffuGqq66lUqWG9O59KStWrMDtjkcxeX5BhXta0L79hYcdY8uWLbz44oukpxdHZFDU/ZqHooQ+j8h7qLyByOe/4PPFs2XLFlq1ao8K8SSjksq+QFE0y2LbaSja5nQ91gjd5paYZ+pyDaZ///5nJOiDClM9++yztGvcmIY1apBgWTQOhSiipRYmaS8/UkCluPaie4iK37fUII8odsv7okIu1cuWZeHChXnnef/99+lzwQV0b9eOl19+mdWrV/PZZ5/lbXB3Pu88WokK4VQQVXR9r55YXhQVBtqsJ4UmIpzr9dK3q5K1/vrrrymTmspl+hrColhFxUSxk34RxTBqI6o+QAnb5uZRhe9D/Z3dP24crW07r3rYGyKkx8Ud4rgA1CpTJqYq2U4Rgl4vu3btOsYnderseIA/XUSGi0hj/bqYiPQ9Ur8TeZzpwH/77Xfh810WBRIHCQSqHVUtz06dLkaFEvIBJhjszAsvvHAKrhx69RqI35+NyKO6RGIyjpOM1zsE07wZx8ngiSemAyrEo8IoTxVYJTTl2WefZf78+TRo0JoqVRrx4IMTDwuQL730EsFgCxQn34/aLK1FzZr1WLNmzSHtp0x5HMuKJxjsgGUV0Ro/b+jJ5zV8viA1azbBsjJQiV8H8q4tLu4c3nvvPe655z7t5Y9GsXa+QiSTWrXqEwqlIbJCX8fXuu9C8imiIPIbjpP1r4j/RgqaR8DqoKg4e3kN5PU14O4R4TlRm7/pGlAH6PeK6MkhKMINN9yQN/aLs2eTqcMm00Qo6/cz6cEH8z7//fffKZ6aSrwG/fLa098kqjpXkj58IqQ6Dhm2Tfs2bfJAdO/evTz22GOUz8wk3eejuOMQ5/VyZ9R/uD9EaGgYNGrYkEWLFh3TPWpeuzavFliBVA+FWLJkySFty2VksDyq3QERQl5voTTTM82OS7JBRIqLSEv9b0dEgkfT70QdZzrw//LLLyQmZuJ2D0fkRSyrC7VqNT6qWP3EiQ9h2+2jPOitWFbSSVd63LVrF08++SQPP/wwDz/8MN27X8INN9xCvXotiJUn+ArT9HPHHXezf/9+PJ4gsaGWvxCJJy2tmObwP4/ImzhOYy6/fFih53733XcJBmuiwid/ItIAkSq43T2xrGTGjs0Hkp9//lnvQ6zT5zuAz1eN+PgsDMNFsWKV8rzRESNuwOuNnoB/wueLY+vWrbRq1RW1wXseShYiEZF4qlVrSOvWnTCMCqhErUjfMEpJNA61wglgmn569Rp0xsd3V6xYQYUCmjsfiKJWIiqTt3TUZy+JYuYkGQaO2028qIQoRPhQVJhlpubIVy9Vipmiqm+liVBXhDjL4rphwyiXnk4Rv59kER7R/cOiwkWdRUkZB7UH7xfhUp+P6SI0dxzaNm3Kjh07qFqqFC0DAa51uUj1+UhyHBobBp2jrjcsQv1AgPnz5x/zPerVqROTosbcK0KKZfHDDz8c0nb0yJF0tCz+EJXbcJvLdczyEKfajsfjv1REPheRdfp1WRF550j9TuRxpgM/KC3zoUNH0qxZR+69dyx//fXXUfXbvXs3NWo0Ihisg2Vdjm1nMHr0mJN6rWvWrCExMZNAoBO23R/bTmTevHkAWu8+WqMHRJKwrMb06jWIG264WXv93VCVscqiMmrjEWmDypZVE5jPFyp0czg3N5cyZarhdg9FceI7kM/C2YRlxedtnM6ZM4dQqH2B6/kP7dv3YP/+/TEZs1u3bqVYsQoEAq1xu4dgWWncc4+S/L3pptvw+S5FbehmoDKFv0GVa7T1kYyie0bOcyeK5eTVK4Wd2HYHRoy48ZDvdCbZr7/+SrzPx86om/aICBeJ0r1pLSq001AUZdIR4WpRbB3H5WJwAU94oAghy+LAgQMk2DaZoiiYP4pS2AyI0NU0+VSU5EJQJCar9ksN9K1FJZEliqKLRj7fJypk0693b3pH6fU8IkrO+Q9RYaEBojacO3o81K1c+bhyJz7//HNSHIdJhsGrIrS2bXocpuzj3r176d+tG0Gvl3ifjya1ahUqcncm2vEA/wpRJROXR713NsZ/Ai03N5dXXnmFSZMmsXLlypN+vnbtumEY90b9tj8iMbEoOTk5tGvXDdO8J+qz91HSBTvw+eLYtm0bF110kQbJUqjSh0kopcyHUEyYsYjk4vPFs3btWu64427OOacdQ4YMz/vB/Prrr/TvfwU+Xzoiz8UAeyjUOi9xbNmyZXolsT/vc693KNddV3gxkd27d3PRRT3x+eIwTTctWnRi8+bN/PLLL6Snl8LjqcWhonF9UNTQc1CMoQYoDn8mImtREhLzdNsvSU0tddKf0T+xJUuW0OaccyiVmkrvCy5gw4YNDB00iGzb5mlRiVyO9vLTRIV7QhqME7THH7kZV4nQqwDwdxaVhPXNN9/QsFYtmmgwbiCK2ZOkPeHoSaZd1Otuoqidkdeb9eSwVdSmcgl9XbZhcIHk5wKMjeq3TYTbRWUGt2rZ8rB6Q//EPv/8c3p16kTz2rW5f9y4QuP70fb7778fljZ6ptrxAP+n+u9y/dcdkXE4Vcf/OvCfalPVs1bHgJ9tp/HTTz/x/fffk5RUFMNohgp1JKIyasPYdgbr16/nk08+we8viQrTtEVtkkbG2qDBcxwVK9ahatX6WNZFiLyE2z2cxMSi/Prrr3nXct11N+H1XhnV/09sOzUm1t+4cStcrhKI1MQ0G5CQkMHmzZsL/W4zZszE76+OUucsj0gI207TTCIfjpOAaQ6K+e5qXyCAysZ9EcUUykbkDz3hFEHJSYDIxxQrVvmkP6OjsaVLlzJhwgSSbJtpokTZbjVNSqan8/PPP5MUDFJVhI6iNnADGqSDonjzizT4R1ex2iyKovmoKBrlBN3Gdrv5/fffmTRpErVFbdQmimLaZBaYKOaKootOE1WIPUkkr1pX5KgqqgB8qv6LqMSq8nryOSAqPBUn+QyfLaLUOQuLw5+1wu14gH+ciIwWke9E5Dwt1Hb3kfqdyOMs8J9YO/fcDsSWNvySYDA1z+P566+/6NChM253dVRsPYzIY5QqpcTIwuEw/fpdgeOUQKlUflUASBMoWrQc06dPx3FqEJsN3Ju+fS/Ju5ZHH30ctcF7ESLjMM2y1KzZgJSUEgSDKXTv3o9gMBXDuBklh9yJqlXrx4R4du7cyezZs5k+fTqVK9dD5HpUCOpjVCWugSgJ5oN6VeInv/rWclQcf3TUNR5AsXvuRdUMSNRt+mLb1Zg0afIpf2bRlpubS4+OHSnuOFT2eLi6AKg28vmoUbVqjMwxopgwY0QVSXGLUrQ8VxTLp5+oUFBjETySn+HbUIQbRahYTFUu27BhA1YUkG8QtRE8WnvqO0SoIypprFh8POfWrUuVMmW4PIpO+pOeTOLkUDG5/+iJJFWERJeLxnXqEG9ZNIiLI96yuGfMmNN67/9tdjzAb+g4/2wReVH/2zhSvxN5nAX+E2vLly8nEEjB57sc07wB205j+vSZMW327NlDu3bdsKxE/P7iFCtWMabQRTgcZsmSJWRnN8blGhb1232TxMQsdu/eTaVKNVGJT9G/7QdwuUL88ssvrFixAtP0o8oZqvq2Llc6Xm8NFMvmB1yuizCMqlH9DxIIVM5jczz99H9xuyOVvGqjmDlZKImISJ8cVGhKbRD7fLV0cZVEVJiqGiohLPo6s/UEMUJ7/VsxjFIMHHjpaVXiBKVdX9fvZ5+oTNu7C4BnWxG6avAdFvX+1aLCJ5u01z9UlGRDnCga50xRYZeIVMMnohK1uouQ5HYzY+pU5s6dS4sCZQgniQodJYkKKWWKEOdy8c033wCK/FCmSBHO83gYrFcDZb1eShYpQqkCk9NYUXIS5US4wDRJj4tj1qxZvPbaazErxbN2dHZMwC8iLhH57u/a/E3fGSKyVURWRb03RkQ2632DFSLS9mjGOgv8J95++ukn7r33Pm688WaWL19+2HabN2/m22+/PSzY/fzzz5QoUYlgsA7BYBv8/kTeeecdHnvscSyrvgbXCCPnD0Sq4PU2ZsqUKcTHp6HCKtE4UlJ76pHXuzSo/5H3XjDYieeee45t27bh84VQG8vf68//RLFzXtGv16PYPAGUnAMEgy14/vnnGTbsWt3Xj5JzjqiKLkNt9p5X4Nqm0K1b/5P1SP7WwuEwTzz6KJWLFSPB66WZKF77h6L07Vfri3xbVNx+u6gQTpKokM0P2oMPao8+TlS4J0lUaCbyJTfo/hHph3a67WsilEpL46233iK7AGPoJlGbxe9ojz8sQm/T5Obrr8+7/r/++ouZM2dyxRVXMHToUF544QX27dtHw+rV6WeafK4nnhQRlmnwn6IntgyPhzifj8kPPXRa7v2/2Y7H458vIsWO1K6Qfk20uFtB4B/5T8c6C/xntuXk5LBw4UIeffRRLr10MI0bt6dcuRooWuhjqDBJUyLiZoFAG4YPH47j1EZtEOdG4UgGsUXQ96ISryKlD7/C5wvx/fff89JLL+HxlNGTRRIijVBJXD0QqYgKZyWhYvjno9hI15GcnMWePXvo0+cyTPNelDxzpLhKY9QehRsl65wfpnK7R3P11YVXrzrZ9tSTT1LBcfgGsABAAAAgAElEQVRIlPhZA1ElDB8XFWaxtHeeIUqLJnLRdUXINAzcGvhvEiWjEK2pH63Jc1AD/Vb9uqqeCOqJ4HW5yM3NpVKJEgx3u1kl+fr8NWJnSF4VoXmtWkf8Xjt27KBDq1YkGwZtRUk17NSrhuWiZJrjRLF64r3eU5LY+L9kxwP8i0TkTxF5R0RejhxH6qf7ljgL/P87tn79ehYsWFBoenwkl8HjGYnIXFyujihN/IMoqedXEcnAMK4mKakoU6dOxe/vguLIt0fJLtyCafqxrGYoeYU/8XgGk55eDstKIhCoiWGEcLuDeL1BMjJK69XA/ajKXi9o0L5Qe/IOsQXV70IkgVatOgFQs2YjRFqhNm/HoiQaqiMyCiXlUBaRAYh8hGFMwO9PLjTB7FRYo6pVeU2EvzSYNxVhhAbIgAb9UaLi9JHQySbtiTteLyENxoiK8aeI4NL9hkT1eVpURu+nosJA5fVEUVWEJMti586d/Pzzzwy6+GLiTZMmorRz4vUqI3KzrxFhYO/eR/Xdnnv2WUKmSZYoBlCGKGppjgjNRWn7LxC1Wrn11ltP8p3+37LjAf6mhR1H6sfhgX+DKOG3GSKS8Dd9LxORL0Tki2J6Y+msnR4Lh8NcddVwLCuZUKg1lpXAmDGxUrh33HH3IdnLIqVxu7sgMhPTrIfHk0jbthfx/fffs337dhwnEaWPMx6Rc3G5knjqqacYPHgEPl8Qt9tHmzZd2b59Oxs3bqRcuVq4XLeiNl9/RtFMz4l2NFHF0316lZBa4LOvESmK4xTjxhtvxuvNQOnuR2L9z6AyduvqvsmIJGPbGbRv3/1vQ2In22qXLcsHoqiSHaKA+k9Rm6shUYqX2aIyc/toMO4pQlYwiFsUBfMdDewrREkiPKD7Fhehlm2THhfHeeecQ5woFcyKIvTWnveFIlQpWZLVq1dz55gxlEtP5xkRfhO1CZyiz99WhIBpHrVWVbxts0xU2OpiUauX1qJkGopIftipiwj9+/c/Yff0k08+oV2TJlTIzOSyPn3+dVTNo7HjzdxNF5GOItJBRNKPpg+FA3+a3jcwReRuEZlxNOOc9fhPn+3bt4/u3XtqrzgiZfwztp0Rk3NwySVXki99rA7H6U6bNu3p2LEXTzzxBAcOHGD37t0sXryYTZs2sXDhQlJTS2BZKQQCyTz0UD5bJjc3l/3797NixQomTpzIU089pTdkD6BCQekoumarAuB+i/b6a2qPP5pxNAGRKvj93bHtJFTt3otRoaJ5Ue12oMJCW/F4GvPQQ5NOx62PsfvHjqWJ49BLYuWU0eDeSwTLMMgUtXmaJIox016ERL+fOMNgsvaoJxXoX1uEjMREXn/99TxmV5GEBO7RABxpFxahpW0T8PkY6vXmZeAWF6G/KHroWFEbvLNnzz7kO3z55ZcMGTSIvhdeyPz58wmHw8ybN4/WoVDM9UwUlVj2lp6UIu+3EuHxxx8/Iffzm2++IdlxmC7CShGGud1UKVnyjNVhOlY7Ho9/kIj8KCL/EZEntcc+4Ej9KAT4j/azgsdZ4D899v333xMIZKI2Oh3tTat4vM93FQ9GabTMmTNH8+cj2jZrcbmCfPTRR3lt5s2bj9+fRChUG8tKpH79c2nRojM9evQvNHHtxhtvw7aL4PNdid+fjWFYKPXN2qjavL9pz34eKhb/uZ4QlqBi9QO1534tKs4fj8iFWFayVgc9qL9beUSWFlitpCDyIyL/pVWrrqfkfv+d5ebmMmLwYCyXixaFePy1REgzTTpoT7+tBs5HRAiZJjNnziTodhMSYVwB4K/qdudVnPrjjz+YMWMGXTp0INXtZmSBtsNF6BT1+mZR8fdoZs5wj4dB/frRrV07apUuzdWXX86CBQtIcRz6GQY19cSUoRk7pR0npvziDaLCWE+LCjH9Iqo8YlooxO+//35C7ue1gwdzi8sV892yg8G/lXf+N9rxAP9qEUmKep0kIquP1I/CPf6MqH9fKyLPHc04Z4H/1Fs4HKZ48UooWYZclHZ+Y+01hwkEGseUpzt48CD9+1+JYQRRoZIghtGGhIQi/Pzzz+zcuRPHSSBfO38XikZ5OS7X7fj9yTEKn+vXr9clFSOa/WFcrrK43S1QoZyItlETDdI+lO7+LP3+w4hcpicBFyqc1Am3O47hw2+kbNmaqNBOhp4guqEKs4cRmYyIyj8wjBEMGTL8dDyCPFu7di3XXHEFXVq0YNzYsdSuUIF6LhcjRVE2a+q/e0QVVCkiKgEqAmgPGAb9LrqImTNnku31ki5KjfJXEe7UXvuVl17Kt99+S2ZiIl38fgZ5vVguF6mGwW96nO2i+PXRpQr/K0rVMxpAHxIh3uViomGwWIR2Lhd+l4s2uv/TopKzyokSawsZBucbBgtEVQMLidDN6yXRsqhetiyJjkObxo3/VgH2n1qPzp3J1t+9jAjTRTg/GOTFF188Yec4E+x4gH+xiHijXntFZPFR9HtWRH4WkRwR+UlEBorI0yLylY7xvxw9EfzdcRb4T61t2LCBK68cgsuVSmzy1VuIVMYwOlO+fK1DUtzfeOMN/P6qKOXM7XplcDl33HE3CxYsIBRqSSxG/Ael6Q+meR/du+cnds2ePZtQqFOB9lMpVaqqVudcqd/7SQN+OtFF3tW447W33wCXayiBQArvvfceAJ999pku9F4aVWGrCSrWn45iIY1F5DpE7EKFu06VrV27ltRgkJtcLp4VJWjWpVUrnn76aVq1bEm10qUpX6wYd+gvvkhUAlX0jZsjQpuGDXnsscfo4zjM05NFvPaoe4kKzyS63dwZlWj1uQgJXi9JlkWbUIgky6JaqVLcZZq8p8cQUTH5z3WfraJCP531OP1FqXOOEZUcVkrUKmWqvs6f9evOIqR5PPTs0oURI0bwwAMPHFFj/1gtHA5TvmhRRunJbIm+5oDPxx9//HFSznm67HiA/ylRlbfGiMhtIrJMh32Gi8jwI/U/EcdZ4D91tmzZMgKBFNzuARoA90VhyGxEkmndum2hP5AZM2bg9/csANYPMHDgYD7//HMcpyB18xZErtH/fpXs7BZ5Y33zzTfYdjrRNX0tqw933nkPjz76BI5TDJX0dbf2+GujKmRNQIm+BTSQp5KcXIZrrhl1iMzDddfdgGF0QmQwKmErDsXieRjFDOpORkaZk37P/86uvuwybooKSewXIdNxWLVqFatWrWLRokV0aNuWc0WFWyK69xFK558iNHEcJj/yCD/99BMhj4cl+rM1olhBS0QlbqWLsKrApFHUcfjwww+ZP38+P/zwA2vXriUjIQG/KI2fA6JCM5aoernx2oN+UISlojZo9+ixwqI2pieJKv4+L+o8YRHKBwIxRdlPlq1YsYLSfn9MeOopEZrUqHHSz32q7XiA/7a/O47U/0QcZ4H/1FnLlp1RoQ4Q6YjS61mlvfhU6tdvmleI+vfff2fq1KmMGzeOb7/9lo0bN2JZCeTrAO3E76/Myy+/zO7du3XlrvYoBc+7UUya1YjkYFkdueuue2OupV+/K/D7KyFyJ47TmWLFKrBjxw4A3nvvPcqWrYlpltXXthm3u6YG/ImocpKbEPmK9PTCwfurr77C601ChYPCiAxBbWJPR+QxHCeLWbOeObk3/G8sHA5To2RJhotKzIrEwZsFg9SsWJEsxyE7FMIvSvv+QlEbv9miaJyVgkESLYsBPXqQk5PDO++8Q6rXS5wG+URRWjqLRVEoLxBVDjEChqtESAkEDpGhvu++++hXID7eV4RrtQc9UU8+NUTlAHQQyat5O1mvAjqJ8GRU/1wRitg233333THdq927dzN61CiqlyzJefXq8cYbbxy27dKlSykXCMQA/zMitG/S5JjOfSbbcbF6TvdxFvhPnRUpUoF8JsyfqFBJiKJFKzF58pS8duvXrycpqSh+/4V4PEOw7RSmT5/JE09Mx7LiiYtriGUlctVVwwmHwzz11FP4/S3IlzpuhYiFbTfE7y9OkyZt2LNnT8y1hMNhXnvtNUaMuI7HHnvskIpH+/bt49JLh+Lx2LjdPrp06YllxaGonpHf9IvUrNn0sN/34ot765WBH8Xbvx2R6pQrV4u33377hN7bo7Hc3Ny8iXXYNddg6xBJLVFhmXdFFSW/wOslV5Q0ckRW+WZRGa8TRPCaJp9++mnMKueqAQMYLyo7t4Qo5kwHUTH1+aKonrYI/S2LUS4XabbN9CeeOOQaJ0yYwACfLwb4L9FePtqbD2jPfr3+mypqT6GxTiZz9PG4qKpgA7xeWtSrl3eOnJwcduzYcdTyGJ1atqSrz8cSUYVkMhyHd955p9C2Bw8epErJktxjmuwVlRBXwe8/ZcWPTqWdBf6zdlTWvfsluFzRgmUfkpCQeYj2ebdu/THN26LafY3fn8SePXvYuXMn77//PmvXrmXs2PHUqdOSKlXqYhj9YsJAhnENffv2ZcWKFcd0reFwmIEDh+gi9y2xrHiaNj0fx6mJknqeguOk8/rrrx92jFWrVmFZKShtoDAim3GcoqdcATInJ4dRQ4cSsiy8Lhdd27QhZJosjAqFXCVqM7JYUhKLRfHek0Tp5V+u//2iCH3dbjo0bw6oe7R+/Xo2b97MDSNGcL3bTSRk9JIoWeW+ojZpqzoOLZo0oWr58lQoX57x48fn0Rtzc3NZtWoVW7ZsYfPmzSQ5Di+IsFuE2RrEHxNVkzYoSt8n+mF3EyHL46FRjRrUqVyZGo5Db6+XkGGQ4jgMHjgwr8bztMcfJz0ujqDXS8WsrCNW2Vq/fj2pts3+qPPNEKFzixZ/26d1o0a4TJO0UIiJ999/2jWYToadBf6zdlT2448/kpFRimCwOX5/N2w7IU8bP9pKlKhGrLQCBAJl+Pbbb/PatGrVBdtui8grGMZ47VUv0u234zjFWbx48TFf64IFC/D7K5NPId2Iz5fE+PHjadq0A5069WLRokWEw2FWrFjBBx98UKjm+oQJD2NZ8YRC2VhWPHfeed8xX9Ox2l1jxtDUtpmsvfZswyCxAHiuECVf0LJBA2aKkm14LurzN7WnXcE0SbMsrhwwgHqVK5Nu2yT6fLRs0IBEx2GqqDDOdS4XxVNS6NmxIx2aNaNCVhalXC5K6BVEZdOkWd26fPLJJ5RKT6eU30+Cz0ffrl155513qFuxIh6XizoVKjB27FjOrV2bSkWLEvB4uKzAtV8qgs80aZidTUfb5qB+/ye9yqhZrhwrVqzg448/JtNx+FJPdvNESPL78yaFwmz58uWUDwRizveaCM1q1jzifc/Nzf2fBPyInQX+s3bUtm/fPubMmcOMGTMOm8144YV9MM07o35r3+E4iezevRuAb7/9FtsuQnQBFcO4B9OMJxQ6H8tKZMSI0YWOfbQ/xCuvvAbFvsn/zTtOv5gkn507d5Kd3RS/vyShUDYJCUX49NNPDxlr586dLF68mO3btx/VuU+0lS9ShFaiNHEmiIrXW5KvRY8offskl4vMuDgSTBNLJKbK1kERTFEbrjtESDAMxhgGB0Vlv/Zyu+nWqRPtmzalbHo6nVq35qWXXiInJ4fZs2dT3bYpIUoWIjJeI5+P5GCQZ/V7f4pQz+ejf79+hXLqlyxZQjHLIkmUuif6b0CEZnpV8t8Ck0JbUUqhGQkJXDVwIPcYRsznHYNBnnnm8Hstubm5lEpP50k9WfwuQlPH4aEJE07mI/tX2D8GfhF5WEQmHe44XL+TcZwF/jPPFi9ejG0n4fF0xeW6FttO49FH8+PB7733HqFQA2J/4y9Sp04LXn75ZTZs2HDImK+++irFi1fGMAwqVlSe5t/ZuHH3Y1l9o8YPEwxmM3bsWKpWbYhtx5OeXg63uxP5vP8XyMwsd1T1kE+FrVmzhtGjRpFk26RLvjwBoqpNZYlKwrpFh1OuFcXGGW8YxEnsBunLoqiTiJJn8IrEhD/WiAoVtWrYkBply1La76dGMEjx1FSGDh1KS8NgUAFQvl+EFB0e+lOUdk5JERqYJgmOw/z58/n222+5ol8/2p1zDn1696ZLMMhsUWGkRFEbvHGi9igqiJJliIz/l6jqW9+J0DQY5IJOnRhdYOO4eTDISy+99Lf3cfny5VTIyiLDtgmaJsmWRe2yZZn11FOn6EmemXYswN9PH0+IyEciMlQfi0TkscP1OxnHWeA/vP31119HXd/3aO23337jkUce4fbb7yg0/v7xxx/j9yfj8/XA7W6Ax2Mzffr0mDa7d+8mEEiOCu3swXHO5eGHHyn0nN98841mBM1HyTI8QyCQnOeB7969mzfeeINPP/00b0Wwfft2UlKK4fEMRWQ2ltWVcuVq4DjJKMG2rShqZgpK1jkHkYM4TpGTXsz+aOzDDz8k2XG4zu3mdu0NT48CvCmiGDptRGgiiuu+QnvHl4pQzePB73KRZRiUlXxp5EjYxyeKJx8Z7wNRWbaNRbhM8rNtp4tQsWhRMn0+yoiihEb2Fc71+fC73ewRVZDlAr0SQBQNNNFxSPb7ucPlYo4I5/h8+A2DH0Qxdd7Wk80DojKJLxRF+ewiKoO4mr4WRGgQCjF16lRSHIfnRG1c32OaFE9NZe/evUe8n+FwmFaNGtHD5+Nrfb5SjlOofMT/FzseOucSEXFHvfaIyJIj9TuRx1ngP9R27dpFx4498XgcPB6Hjh17HsJ6ORZbu3YtCQlFsO1euFwjse10Hnnk0Zg2lSvXR+TZKKfsJUqXPpQD/cYbbxAMpmiJhhS6dLk4j7ESbYsWLSIQSENp7KiyjSLg93dj2rRpvP/++3qcJgQC5ahevWEerXPLli0MH34D557bibvuupdRo27A7R5eYKXRGJEqiJiIZOJ2+8+IRJ1zs7OZFXWhK0WxX3JEae2nieLZNxMVKmkgSgjtTlHhoAQR2upkqqkiBA2DJj4fD4hQ1O2mkSjmzrt6NVBcVAnGYnKoFHPI66Vv9+4kmiblRK0w6rlcpPv92JJP/4yWfEaEih4PA3Wy1nN68krRk1ALTR29Jap9rihGUaQmwG2i2D73miYl09I4cOAA77//Pk1q1iQ9FOLC889n7dq1R3U/N2/eTKLPF7NqmitC8+zsk/wkz1w7XsmGxKjXCUcr2XCijrPAf6j16DEAn68vamNzFz5fH3r1Gnjc4150UT9M846o3/YaHCcxZlXhdvsQ+SuqTS4iUmj4ZM+ePXz00UeH9bB37dqlVwYLUKyaH1AF21/D7+/K1KlTSU4uhuL+g0gYr3fAYWUUrrtuNKYZzUraj0rMekSvJJbidmecFqpmQSsSH8+GKJAKa8Ds5jikezy0kvwi5ktExfwjE8UaPTFESzNMEqFS8eI0ys6mRfPmdDEMxmugj9dA203UPsKCqH4/ihBv2+zbt4+lS5cybNgwLh00iNbNm1PHMCinz9tEhHuj+u0SIWiaTBFVqzdB1IoEUbz9FK+X0qmpPFVgsmig294i+bINHZs3P2qAP5ytXbuWIo6TtyJB1AqoXoUKJ+iJ/fvseID/EhHZGCXS9oOI9DtSvxN5nAX+Q83rdcjXsQGRX/F6/cc9bsmSNYjVsIdgsFxM2cUKFeqgsngjbV6hRImqx3S+efPmEQoVrHT1MCLNCAZT+PTTT/H7SxT4/DNKlqxe6HgrV67EtlNRVbzCiNyGSOVDxr/oon7HdL0n0rq2acM9URIJr4hQKjWVqVOnUq9ChTz9/MhRThRdE1FJVwWLn8wXxclPFKGox0Oc200dUVo6d4haTfhEqXf6NYhPFUXjHDN6NLNmzaJV/fqc37Ahs2fPpnhyMsmi9H8Q4Ss99o2iqmVVc7k4t359ztVMoW5R1/KcnmiaGgbV9SSBqEIrkbyDai4XibbNwoULT8j9DIfD1CpXjvtMkxxR8hFNHIcHxo07IeP/G+24WD2iZJk7iZJmPmpZ5hN1nAX+Q83vT0SVFYz81tbh9yfFtFmzZg0jR95I//6X0aHDBaSnl6VGjSa8+uqrhx23Z88BmOYtUeN+g9+flMfWAfjggw9wnCS83svweq/EcZKO+cf79ttvEwzWLgDMt5KYWIzPPvuMVatW4XbbKN38Wihdnc40adLusGM+//wLpKSUQFXRCqDE4KLHf5AePQYc0/WeSFu7di3FUlI4LxjkgkCARMfh5ZdfBuCS7t1jaunuFhXuGaxf79cAPl+//lOUJv4EUaULS4qQ5fXi1RNKpvbGw6KULoMixJkmtmHQODubkcOGUdlxeFGUCFs5x6F4cnJeUfbIdSzW11HFMOjUvj379u2jR8eOJHi9VNXjHxAVGvpCVGjnCj0hlRUVClogQjOfjz59+hyVHs/mzZsZPmQI59WtS5+ePRkwYABDhwzh1VdfPWSVuX79es6pUYOg10vQ52PY5Zf/z0kt/xM7XuDvKCL366PD0fQ5kcexAv+BAwdO+MbnmWLDh9+I4zRDZDkiy3GcpowceVPe50uWLMHvT8btHoXIPSjxsdGIzMVxMvLEygrahg0bSE7Owu/vgtc7GNtOYdq0GYe0+/HHHxk/fjxjx449LhGz3NxcSpasjNs9ApFvEHkax0lm5cqVDB9+I5aViGmWRmnmv4uSV6jJuee2+dtxd+3ahcvloGQbSiNyL0rG+V08ntQYuejTaXv27GHWrFk0qF0b2+3Ga5pUKlOGRx55BEdUDPy/IjQSpYXjF5XF21x7zn7DoLRt4xeVyBUJDU0VxaLxGAZXSH5B9rAIlUUpaB4URfusJ4oBNCcK4D8XISMUIsk0Gaf7hUW4TtSGbMA0+fHHH/O+x9dff03ZIkUY5PHwnKg4f/Rs+75eAUwQoaPjUKt8+UMytQuznTt3KtaR2019USGruqLCROU9Hnp17lwo/Xf79u0xzsr/VzueUM99osouDtDHWyJyz5H6ncjjnwJ/bm4uw4Zdj2WFcLstGjQ4j02bNv3DW3ZmW05ODrfeeidpaaVJSyvNbbfdFePZNGp0PkpzJvLbW4PSxtmHyOO0bdvtsGP/8ccfTJ8+nfHjxxeqnXLw4EG2bt1a6EZtTk4OI0aMxu9PxOOx6d79kiNuOm/ZsoWePQeQllaa+vXPY9GiRbz33nv4/aU1WFdB5EMUQ6gIIpUQ8R92sxjUxKS0gcKoYu8dUDUFQjzwwIOF9jldNvTSS2nu8ZAkSvqgjwh+06SCZTFYFJNmqqiCK/1FSSq/JIoKeZ7fzxVXXEFZy4oB2sdEyHC5aJqdTQ1RdXkRFbYpJrH6+W+LUFp744/o97aLSrhq1qABQcOgmCh6Zqq+hhKOw7Jly2K+x/bt2xl59dXUq1iRkNudF+9HFKunVvnyVC5WjCTbpk758nmrm4K2adMmhg8ezPkNGtCmVSu62zYzRYW6zou69r0ilPB6+fjjj2P6h8Nhvv3225Om7vlvsuMB/i9FxIx67RKRL4/U70Qe/xT4779/Ao7TCJHNiOzD5bqVatUa/qMx/u2WmloKJVQWjQdp2gOeTaNGf+8xF7R169bx8ccf88Ybb1CkSFl8vgSCwVQee2xqTLtbb70Tx2mqw1Bb8fn60rFjz398/TfcMBqRW/V1l9ArmwxEXtPv7ca2z+XBBycW2j8cDpOVVYF89tFBRPrQqNHh0/hPhW3bto0vvvgixhtNC4WoIUr6IPKwXhQhzjD4Sr/OEaG0aXJTAU/6Yp+PyZMnUyErizGiEro+1gBdv2ZNtm3bRpPsbPwivKA9+TiJzRd4XlR1q0/0imK2CMP0SuAu0yQgih10naiN4LAIZQsoaX700UeMuPpq7rjtNjZu3MjNo0cTFBXL76nHTQ0EGOl284OocE+64xyy8tq+fTtZyckMd7t5WYSqpsktInQVtVcxvuD3F2FcVAz/m2++oVLx4hRzHBIti65tDtWAitiff/7J0qVLT1hxlzPRjhf4o1k9iWc68JcvX1eHBSL/P3KxrNRCk4b+V01l1t4cdQ8WagDdguNkM2PGoeGbwmz//v106NAd204lGKyOqlj1mvakV+E4mTGyC2lppTVIR867C4/H+cchtylTpuA4XfUYoxBpiiqOEv27f5k6dVoedowvvviCxMRMgsFa2HZxqlVrkEcDPdUWDocZPWIE8ZZFtVCIJL+fF55/HoBSqam4JJ8/j6i4uilCnM9H40CAZLebBMMgJPmbrZ+J4BgGmzZt4ocffqB9s2ZYLhfJjkOFzEwy4+Np36wZK1eu5NVXX6VUcjJxouiUbUQxheaLShJboAHdKyqEVEtUDsAqUQycuqJopQki9DJNqpcpkxdimfTgg2Q5DncYBpd5PMSbJiHT5FIR7hLFNookj/0U9R0fEqHfRRfF3KcH7r+fvlGrlzclPwGsvqgi8xHWzl+i9hLuvvvuvHtcvUwZphhGnkT1BZbF6FGjDnke0x5/nATbpmooRLxlcf+99x7S5n/Bjgf4exbC6ul+pH4n8vinwF+t2jmIvBIFEPuwrES2bNnyD2/bv9c2btxIRkZpgsGmOE4HDMPB4wlhWXEMG3bDIXHRgwcPFhorHT/+AWz7PB0ielqHTPLB1zDu5KqrhuW1P1HA//vvv5OeXgqP5ypEnsMwSqHq4EZLQEyic+eL/3ac/fv3s2jRIpYvX35aNVkWLFhABb+fbfril4kQb1n88ssv3H/ffSQbBnOjbuw8EWqWKcMXX3xBkm0zyuXiKQ1+EfBOF8FjmjHn+fXXX0kNBpkiSoXzERFSQyG2bdvGIw8/TH2PhzRRMXhHVLWuyHlfErUvENJ9ERVaKS6q6EprUTH2oMuV50Tt3r2bRMdhrW5/qahQUHMRXi/gnVcWJSwXeT1dhG5t28Zc//ChQ7knqs19IrTQk1C6nqRqiFpJZIkKZ0WouRs3biTVsmLonJ+JUKVYsZhzrFmzhmTb5jvd5kdRdQdOtTDfqbDj3dzN0Bu8/wpWz1NPPY3jVEDkI0TW4fX2o0WLjv9ojP8F27dvH/Pnz2fWrFls376dzZs3HwLAv/76K23adFJDzDoAACAASURBVMXl8uD3J3HzzXfEMCXq1j0PkZf172gOIi0KeN3D6dWrL6A8rg4dLsAw6qHi6r/i9V5MlSr1mD179iG67oezDRs2sGjRItatW8eIETfQuHF7br55DE2btsGy2unVyxQcJ4XPPvvsxN2wk2hX9O/PhAJAeGEgwNNPP01OTg5XXXEFfsOgnwgDXS6CPh/n1qlDxYwMOmsPFu3FpmtP+BkR6lasGHOeSZMm0a9AvP9i22by5Ml89tlnBPSkg6hCKX5RbKALRcX4Z4rKJXhJt4kXYYjkx9UX6j5bt24FVAiwmN+fd64UPWncKioME+n3tag8hCnaY18rQnm/n7lz58Zc/5tvvkl5v58dul9jUaykL0SFr4aICvmco8erUqpU3v/XHTt2EPL5+D3qu78kQtMCYm0TJ07k8gKy0qMNg5tHF64d9W+2/1esnnA4zKOPPkFWViXi4jK45JIrz4hMzTPR6tZtjtt9LSoRbC2OU4eHH56c93nXrn0xjAf172MvIpkozfr1iDyFSAK2Hc/27du5/vpbse1aqLKHcYh4MIx4vN4BBAJNKV26Gjt27GD//v188cUXh2y+5ebm0qvXQCwrmVCoLo6TyJw5+cCwd+9e7rzzHqpVa0zbtt3yPLSDBw+ycOFC7rvvPl577bUzRocn2m658UaGeb15QBMWoXYgwEUdOhD0+fC6XLRp2pTbbruN3r17U8SymCbCs6L0baK94HoitHO7SXIcbrrpJoZedhmPPfoof/31F/fddx9DPJ4YULvS62X8+PHqM627EzkuExXeaapBNU5UfD9Zg268CMsLTFhJIqxevRpQzLmM+HgW68/Ki9or+F1UacWaokTY4i2Lu+64g1rlyuH3eAhZFnUqVKBNw4Y88fjjec8sHA4zauhQEnw+GodCxJkm4w2D6/RkEn0dndxuJk2aFHOfL+vThxa2zTuicgmKOg7z5s2LaTNr1izaFFDz7GPbPDSx8P2if7P9v2L1nLWjs02bNumC5geifgNvU6FC3bw2y5Ytw3GSNfiPRDGDElHSCs0Q+Sgvw9a24xH5UY/zqw7NbNKvw/h8fendux8JCUUIBitjWYl069Y/T+t/5syZ+P0NyM8K/gLbTvhbSd6DBw/Svn03/P7KuN3DCQSq06JFhzOOu71x40ZSg0GGGwaTROjv8VA0MZHmts0WUTz8y0Rx5NNtO0+HH1HFTAIirBaVRBVwuxkzZgyNs7Op5/XygAitPR6qlynDypUrSbLtPCD+SIQk22bdunVMmjSJblGTDyJ09XhwXC6Ka0/+ev3+X6Li/smGwbSo9j+JEPR4+PPPP/O+27x580iwbXr6/VT0+SguSiLidREq+Hx0aNMmryBMOBxm3rx5pNk2U0RRSOs7DsMuvzzmfm3atIl33nmH999/n2S/n5YiXFMA+NsWotp54MABxt17L/UrVqR1gwa88sorhzyL3bt3Uzojg+FuNx+KcKtpUiQh4bQps55M+3/F6vk324EDB5g3bx4TJkw4pgIlu3btYvz4B+jSpQ/33/9gzA+0oG3evBmfL5HouLnIQipWrE84HM4D5GXLllG3bmNMMxMlnfA5KtavlDEDgQ5MnToVt9tBCaGBKszeuEBYaDYuVwCRN/Xrv3CcZkya9DAAbdt2R+TJmD6h0HmF1gOI2MKFC7Umf6Q28AECgdqHeHmn2/bs2UPLBg1IcrmoYJrEeb0UTUzki6gvu1dUYlV0jB1RjB6fBv+Qz8czs2bx+uuvU0ZnqEZWEE1FmDhxInfcfjvpwSBxXi/FkpKYO2cO4XCYJnXr4hfFjPlWlEhagmWR4vVyiyjxtziRvEkjV4Q0n48En49bDIPJIpS1be685ZZDvt+WLVuYNm0as2fPZsaMGTStWZN6FSrQq0cPrh85ktdeey1vj6VVgwYx0sy/idrEPtwE/9VXX9GtY0cCpslTImwUYZxhkJmYeMx5Ops3b2bIoEHUr1iRQRdffEaI9p0M+3/F6vm32p9//knlynUJBhvi812F4xThpptuP+r+e/fupVy5mtj2hYhMx/6/9s47PIqq++Pfu5tsmd1NQgqhBBKIEGqooUmTDtJBVEBBlAD6CoiI+FNRKQIvoKIoSJPeRFCaIAgiHQGlCLyUSBUlQGhJSNvv74+ZhE1ISEiym5Dcz/PMk52ZW87OTs7cOffcc8xdWaFCrYfa1hs2bEN390HaCP0IzeZq7NTpGdpsftTp9GzYsA3Pnz/PKlUaEFjvoJRvayP6ifTyKsbbt2+zevWG2puBncA5AjYC11Lq6PVdaTBUSfMwWM169VqTJAcNGkq93tETKZEWS1keOHAgQ/nHjx+vLf5ybPN9vv/+qKxfeBcwbvRodjSZUmLrbIS6CGq7g+B3NOXeBGq2rWT7+DSo8W1eAvjmUHUiPTw8nH3TjIBHAPQ2GlnC3Z0e2kPE282N06ZNY+NatVgC4FOaci8JMEQIWt3dedihjXkAq2nKv6fRyOb16vH48eMcOmgQ+/bowbVr1zIpKYnbtm3jjBkzOH36dK5evfqBxVJRUVGsFBTEDorC0QArWywMf+EFkmTVwEDud+jTDjWBfGaxenbt2sVmYWEs7unJLi1bppibMuKbOXNYvkQJephM7PH004XKuSMZl3v1AJgL4CqAY+mcexMAAfhm1g4LkeL/738n02zuqilOErhKk8k7y26oixYtotXawqG+nVZrUy5btizDOtevX2e3bi/QaLTS2zuAzzzzHBWlCtU1ADHU6z9i+fI1WLp0FaqrZu8rZcDKWrWa8OjRoyTJ06dPMzCwEq3WJ6goJenvH0xFKUvgAypKD/r4BNBkKkZH05IQn7J79xdT6lutftTpxhFYR5OpM598stVDvXE2bNhAq7W6Q5uJtFjqceXKlY9w5Z1Po9BQbk6jqMsZDCwD1YYeATXWTXLceitUj5sqUBdXnYCa07Zq2bK8ffs2SxctSj8gZSIzDmrQthegeutMhprLdiLUydpGbm4pbwenodruPwXopdOlWsz1F0BPNzfWCA7myDfffGDxXWxsLFvUr89gRaEnVDt+Y6ORxb28ePjw4ZRyEz/+mM87TDLfhZpM/ejRo3xryBD2MhpTkscvA1i+ZMlcnZtZs2YNyygKd0ON2TPCzY21K1Z8rLJtxcXF8cyZM1kKSZ0RLvfqAdAYQM20ih9AKQCbtIeJVPwOqKaOhWlMHe0f8HzIiNGjR1OIkanq6/XDU/ycs0K9eq2oeu8kt2GnxRLE8PBBNJnaUo1rn0idbjwrV677QP3kNIcnT56k3W7ntm3bOGLEO/zii2mMiopiixadaDJ11ExBX1JR/FKN6I8fP85evV5hnTotOXbs+EyX3SclJbF58w60WmsReJ9Waz02aNDigRzBeU2Pdu34pcMPcxdgEYOBNiFoAKiHauLZqp3fDNW8swyqySUKasgGg5sbR48axW5GIwdDXU3bVxvBG6H62qfNd9sBavz7g7jvA98aYKjBQD+bLVWo5QlCsHvbjBf3fTltGlsrCpvh/ipfQk2a3qi6Gpr75s2bbBgWxteQem1CN5uNy5Yt4+3bt9myQQMGKAqr2WwM8PHhb7/9lqvXu1OzZqmigtoBlrFYUj2c8jPLliyhv4cHS1ss9LVaOdshq9yj8MiKX1PaGW4Z1UvTRlA6in8lgGoAzknFn5oPPhhDo7FvKnOKyVQ03bAJ6fHLL7/QYnlCU84kcJOKUoY7duzIsgxPPtmWwHIHGZKoKKX4xx9/aKGgPWg0erNKlXrZsovGxMTwo4/GMjS0EZ9++tl00yA+KomJifz+++/53nvvc+XKlflO6ZPkvn376KcoHA518VRpnY5BJUqwiV7PS1Bj5vSGGi8/+eKbtTeA5DDG/QF6mExsERbGdVqZQ1DDMwQB9DeZWBuqd05yG9eg+uH7QfW4CYEaMtkbYOOwMG7YsIHeisIeFgvbaEr41KlTKXKfOnWKz3XowHLFirF7mzbs0LQp50H1BLrr0E8cQCGEmlzGamVro5H1oIaHOK3J4Wcy8fTp0yltHz9+nLt3784w7EZOaN+4cUq6yGTFX95htXFMTAynfvYZu7dqxXeGD0+ZfM4PnD17lj5mMw9qsh+Huso5O3N+2VH82x6ybc2oXpo2Uil+qBE+p2qfH6r4AYQDOADgQOk0CzAKKteuXWOpUiFUlC4EPqLFUpH9+r2W5fp2u50DBgyh2exPm60bzWZ/Dhr0xiO93q5cuZKKUo7APgJX6eY2jKGh9VPaiIqKeuR/ErvdzkOHDnHHjh1O+Sd/XJgzZw499Xp+DNWWXg5IlerwNlQzTbS2X0sI1oGaXvEXgC1NJr71+usc9NJLHOWQnvCW9vYw/auv6GMw0Ir7LphdoAZvS9KU32faQyCscuWUbGaXLl3i7NmzuWzZslRvWFFRUSxRpAjHC8E/AX4iBD0NBr5iNLIS7mf7ItSFWU8UK8bKgYGpFqKNA1hBCJZUFL47fLjLrvWyZctYUVF4Amp4ivE6HSsHBaUsVGxRvz7bms1cDHCwwcAAH58M80u7mk8//ZThadZijNDr+eEHHzxyWzky9WR3c1T8ABQA+wB4MguK33ErLCN+UvXKmTFjBt96ayQ3btyYLZvk8ePHuWzZsiy/KaRl+vSZ9PcPptFoY8eOz+XoH+LatWusVq0BLZZgenjUpK9vKf7+++/Zbu9xpn2TJqlSK/4L1byTnDD9JlRzzQaAH+h0NEMN0FZCG6EX8/BgbGwsz5w5Q38PDw4xGPglwOoWC1/rp4aZ3r59O2tUqkSLEPSC6qJ51qHPBG203tBqZZDZzJI2G911OnqZzXx3+HAeOnSI169fJ0nOmjWL3R0WZxFgD6OR/jYbm7q50RdqnP+xAIsrCr+ZM4cWd/dUcwaXAHoZjani+rgCu93OKRMm0M9mo5tOx5b166e8oe7YsYMVrdaUOQYCDDcaOSYbitUZzJ07l13SXPe+JhM/zUby+JxM7poADAOwCsB3AIYCMGVWjw8q/qraZO85bUsEcCErcwaFSfEXNPr0GaB5DSUnO5/PsmVDH6tJttwiNCgolTcLoU7I/gjwItScujXKlWOj0FBWDgzk8wCHQ53UvQawutXKTZs2kSR/+OEHVg8OZtkiRfjiCy884LkVFxfH8+fPs3pwcKpJ5QvawyYW4BNQPYFioLqPVgZYzGikp9HId4cP5+TJkzkwjd//ML2e74wcyf9OmMCuTz/Nds2b87WXX+bevXuZmJjIYp6eqaJyrgDYMDQ0y9fojz/+4KCXXmLvLl24evVqLlmyhD07duTr4eE8fvz4I19zu93+wFvmwoUL+UyaBVxfAnz5+UcPJugMbt26xQAfH47W6XgE4GQh6O/hwX///feR28qJ4l8BYA6Ap7RtFoBvM6vHNIo/nXNyxF8I8PMrQ+BEqjkDo7EIr1y5kteiuZw3Xn2V/QyGlBHxBqgrZP2hul6W9fVN8UuvFhzMIKgxaZ6BGpemLECDTsfmTz5JP7OZ06CGM2hhNrN3167p9rls6VKWVhTOh7pYqgLUGP8joHr2OI56f4I6n/AvwIoWC2fOnEkfszllrcERgEU1z5yMmDtrFksqCj8Wgu/q9fRTFG7dujVL12f79u30UxSO0+k4A2BZNzeWcnfnbIAf6vX0tVhy5c3hwoUL9DaZ+Jf2vWIA1rFYHlgMlpecPXuWvbt2ZcWSJdnj6aez9dAjc6b4j2flWDpllgK4AiABwCUAL6c5LxV/IUANmLfaQfFfotnslaUkHAWNGzduMKRkSZYSgtVxP/3hHKirdTdu3EiSvHTpEi16PYM0Ze+lPRwsUO3zvtoINfmiRgP0MZlSJUZxZN26dawcEMAimgdRDc185IPUOXvXQs2rS6iRM/v37s1lS5awmKcnSyoKi9psnP/NN5l+z927d3PwgAF8a+jQVCk7M6NVvXopOYXXQ/VG8sL9BDFf4MGgbtnlq88/p5fJxDYeHixuNvPF7t3z3Wrv3CAnin8RgHoO+3UBLMisXm5uUvE/vqxfv56KUozAlwSW0GKpxrffzl+Lq1zFkiVLWN5s5iyAczUF7GUwsEq5cly8eHFKuU2bNtHmoPBuQY3PMxWqjb4ckMoFkwAr2Wzcvn07Dx8+nGqCNiIigv959VXWNpv5L9RJ3i+0h0hjgAOg2uH3A6wIpLhADnd35zvaZGx8fDzPnTvHuLi4bH3v27dv89y5cw/104+NjeUT/v48pj0Iy2h/52qfZ0KdQK4TEpItGdLjn3/+4Zo1a7I9mn4cyI5Xz1Goq3ZPALBrI/S/tM+Zjvhzc5OK//Hm119/ZadOPfnUU524ePHiQmnfJ8knq1ZNyZG7Txu59wQ4yN2dPmZzSi7kjRs3smwaxf491BDJBPgRVH/95GQq66DG4/E0GlnJZqO3onDe3Ll8vX9/+ppMrKfT0QNqXPxk10ZvgC2FYE/ts4dm5tkBdfGXtxbfJyfY7Xa+M2wYPU0mFjObWb5kyQfcdxMSEjg4PJw2o5FWIfiqpugd50IOQnULfdFo5NvDhuVIpsJGdhR/4MO2jOo5Y5OKX/K4M3P6dBYRImWitSlUl85k5bYZYPkSJWi329V48W5uvAN1Adc4gMNwf2HWCYAeQrCo2cxQm41FbTb6GY0p8X2OAbQZDKyoKLylHTuvPWhOa+adoiYTn+3YkRaDgWY3N7Zv1ozNnnySRYTgE3o9g81mNg0Le6S8tXa7nTNnzGCN4GCGlCjBLu3bs5qi8B/tYbMCYPEiRVK9OXw8ejSbmc28CnXiuRTUxWy305iydADL+PkV6GxZziBP3Dlza5OKP+vY7XaePXu2QEYafBy5ceMGjx07Rk+jka0A1tZMK15ASlKW5FG4Qa9ndHQ0N27cSC8h6A81NPIwqDb+ZtoDIlQI9u/ThxEREdy7dy/fHj6c7wmR6g2hml7PiWneGnpDndh90WBgm0aNSKomlujoaEZGRtLLZEpJqJIEsKPZzEkTJ2b5u349fTorKwq3aaP0ZjodW6WRIczDg9u2bUupExoUxF0O5+O0h9poLQeBXXvw1YUa8uFhcZskD5KR4tdBUmA4evQoypatiqpVG6FkyWD07h2OhISEvBarUHLz5k10bd0apfz9UbtKFdjj4pAIQAAIhmovXedQfguAoGLFYDabMeWDD9CORB0A2wFMAfA/qKsZwwGUJfH9ihWIiIhA3bp1UbRYMVw2GvEXgGkAlgCI1elwxN09pX0C+F2nw9eenvB66SUsX6f2bjKZoCgKDhw4gFpGI4K18joAL8TGYufGjVn+ztMmTsSMmBg0hbq8f6Xdjp0AorXzdgDXk5Lg6emZUsdsNuOuQxuJAOjujq9sNpQHEAp1knEpgO6Jidi2bVuW5ZFkjFT8BQS73Y527brj3Lk3EBNzCXFxF7Bq1Vl89tnneS1agScqKgoLFy7EsmXLcPeuqsYGv/IKdFu3QklIwH8AjIU6SdYG6oIYPYChBgP6mkx4yWBAV4MBLw8eDJK4evUqbgHoDPVBAQCeAJoCGA9gNYBpsbEYO2IEfvjhB6ycNw9L791DDQCHoPpe/52YiK1WK4a4u+M7AM+aTLBWqoRL169j6owZ8PDwSPUdgoODcTQ+PkVJA8BeNzeUq1Ily9chOiYGXg77FgBJUB9EhwGEGwwoXq4cqlevnlJm4FtvYaiiYCeAkwD6GY1o16oVRk+ejGImE2YBOAbVL/yw0YjAwMAsyyN5COm9BuS3TZp6MufYsWO0WMryfmROEviJVao8mdeiFWh27dpFX6uVXaxWtrPZWKJIER47dowmNzd2g7r4KvkH+Rdq7J1IzWa9YMEC9urVizZ3d7ayWlnNZmPNkBAOee01VtPr2R33QzPf0uoWhxq3pwdAP5uNpRSFyzXTkWN45RF6PXt3786333iDHZs04cdjxjwQaTMtr/TqxVoWC78AONBoZElv7wxdRNPj7TfeYGeTibc0k807ej3rVKrEFnXqsELJkhw8YMADye6T5wWqBAaylLc3hw4axLt37/Lu3busULo0+xiNXAKwh8nEGuXLZ9uzqLACaeMv2NzPphXroPgXsnHjp/NatAJNteBgfuugcD8DWDEggD4WC6sDqezXhLqAahrUcMjlFYUWgNsc7PwD3N356ssvs22TJrRB9bQZAdWrpRjUGDyXoIZf9nVz43JtMre8Vn8f1AnhtQBrlC37SN8lKSmJy5cvZ/9evTh61KhHXmQXGxvLPs88Q5vBQA+Dgc3r1s1R8LPr169zzAcf8JnWrTlh3LiU9KknTpzgeyNH8t23336kdQKFEan4CwFt23anydSFwB4CK6goJVKW+Etyn5iYGLrrdCmhjgk19rsJYMtGjeir07Ek1EVXvaAuSvKAGnVzCMCRUBdRrXcY2e8HWK1MGd65c4cGnY6jtQnZEG1iN7mfWICKEPwOagIXL6junsFQA7PZAFYKCmJYhQr0MpvZvmnTTBOX5Ba3bt1iZGRkuufOnDnD/fv3ZzuC6oYNG+hrNnOEmxuH6nS0GQx85513CuWCwKwgFX8hICYmhiNHvs8yZaoxLKx5il+4xDnY7XaW9vHhHgeF/B3AUIABPj4s6ubGn6DG4XkDanIVH0Whj7s7g6D68I+HunDqda3+dIBdWrZkfHw8iyhKSliBGgB/degnHqCHXs9GJhNvaqafuri/Evec9oCZq5mYJgvB0n5+OUrqkRNiYmLYuWVL+pvNrKKFf85OSO7QMmW4XntAFgXYCmB9IRjg48MTJ044QfLHG6n4JRInMGf2bHoAfB9qovLkcAr+RiO/dlDUdqgrbgcJQQvAQQ7nbkG12/9Hp6OvxZKSlGTM+++zmsXCbwE+D7AmVF/3uwCHubmxed267N+7Nz2MRvrodKkifxJgG6i+88n7T9lsTstFfO3aNS5evJizZs3isNdfZ69OnTh//vyUMAgfvvceO5tMjHN4QAYVLfrIYRLc9XpGQ3WLdczb+4kQbKu5qEruIxW/ROIkmjdowGo6Hd+AGuiskaKwRnBwqgxQBFgdqj3fM41CJsAGej07P/10qlHrhQsX2Lp5c5a2Wlk1MJDdO3Sgp9lMd72enVu2TInWeOPGDQ557TUONBpT2ouDmpXroEMfbW02rlixIte//8aNG1nEbGYLRaFVe7uZA7CuxcKXe/YkSdYJCUmVX5gAg61WHjt27JH6alitGmdBXeTlGGDuGkCb0Zjr3+1xRyp+icRJ3Lx5k32eeYYmNzf6WCx8f8QIrlixghUtFp6GGl/nK6ihCE4DtLi5sYubW4pd/yJAL5Mp1WRqZGQkA3x8+KabG9cA7GU0smZICOPi4lLCDP/1119s17gx3XQ6lvT2pp/FwgFGI2cCbGAysYhOxwNQF2MtAVjUwyNTz55HJTExkaV8fLhVe4v50EEZ3wXoazIxIiKCHZo25TcO525DjdP/qLkeDhw4QD+bjUUA7nVoby3A2rkYx6egIBW/ROJkHGMQ2e12Tvr4Y3oajXSHmi93GsCaisJ333qLdSpXZh2bjf3MZvqZzZwyYUKqtiZNnMi+DlmY7ADr2Wxcu3YtSdUDp1JgIMfqdLyrjewDzWb27NGDfbp359y5czln1iwGeHvToNezRrlyOUpzeefOHYa/8AI9zWYW8/TkR+++y6SkJP71118sqShMNi394KCMCbCBtlJ3+/btLKoonK4p6aaKwle0t4FHJSoqigPDw+lnMHCMEByl07GoovDHH3/M9vcrqEjFL8n3XL16lUOHjmDNmk8xPPz1R/Ihz68kJCRw4cKFbF2/Pp+qVYtzZs+m3W5nQkIC161bx6+++oonT57ktm3bOGXKFG7ZsoVJSUkc+uqrnJBGifZ0c2PZ4sXZv3dvrl69mhWt1lTZruYB7NqyZar+k5KScsXj5fmOHdnLaORlgCcBNlAUThw3jtHR0SxiNjMC4ASoyWSSTTCHAXqZzSlvGTt37mSPdu3YvHZtTv3ssxznRt63bx+HDBzIN19/nUeOHMnxdyyISMUvydfExsYyMLAiDYaBBDbRze0t+vqWemDBT0HDbrfz+U6dWMFi4esGAytbrezWti3Xr1/PChYLb2hKNAKqK+higO/rdPS12VjBYqEd4CmAKwFOBNitVauH9nfx4kU+2749/axW1ipfnt+vXp2pjHfu3KHF3T1V4LTfAIaUKEGSnDJhAoMUhWOhTmCXANjaYqGXycRl+Si5SWEkI8XvlherhSWStKxZswbXr5dAfPx0AEBiYitER1/GwoWLMHjw63ksnfP4+eefcXjLFvweHQ0TgPj4eNTdsQNJgwah/Usv4YnZs1FeCByJjcVkAD0BwG7Hufh4bFUUNImOxv8AhAH4FUBbsznDvpKSktCmUSN0vngRU5KScPTUKfTr1Qt+mzejQYMGGdZT9Ufq+C56AEl2OwBg2Ntvo3pYGL5bvBjdihRBrXr1oNfrsahRI/j6+ubo+kicg4zVI8kXXLlyBQkJT6Q6FhsbjMuXr+SRRNnn/PnzmDNnDjZs2IDExMSHlv3tt9/Q7t49mLR9A4D2d+/i4MGDmPTFF/jj1Cko9eujGICt2gYAQfHxqN2wIf7S6XAKasC3cwD2bd6MvXv3ptvX7t274Xb9OsYmJaEk1LhBI2JjMfvzh8dzstlsaNO8OYYaDLgJ4CKANxUFL/bvn1KmWbNm+HLOHIyfPBndu3dHly5dHgulHxUVhU8mTcKr/fph6dKlSEpKymuRXIJU/JJ8QevWraHTrYKa6wcA/oWiLESHDu3yUqxHZs7MmahZoQJ+GTIEHz33HOpWqYKbN29mWL5q1arYZjYj+fFgB/Cz1YoqWnC0GVOn4uaePfgvVEX9AoBvAMw3m+Ht64sXSSTHuvQG0C0+Hr/88ku6fcXFxcEsRKpjCom4e/cy/V6zly7FvfbtUczNDVVMJtQJD8c7o0ZlWi8/c+PGNDq5awAAGZ9JREFUDdSpUgWHRo1ChW++wdT+/dGrS5e8Fss1pGf/yW+btPEXDqZO/ZImkxc9POrQaPTke++NzmuRHomoqCh6mUw8pdnBLwBs5ubGwa++mmGd6OhoNqpZk3UVhaMBNrRY2KxuXcbHxzM6OpqeJhP/drCtrwHoJQQnjBnDefPmsY1m50/2/GlqtXL58uXp9hUXF8cAHx/O0SZgTwAMVhSuW7fuod/r4sWL7NWlC0t5e7NJjRr8+eefc3Sd8gvjx45lHwfPqXsAgyyWAhXzH3JyV/I4cOPGDe7cuTNlcdLjxPbt21nf05OEmh6xCNRMW1Yh+K6Wv9aRNWvWsKiHB0OsVipubnwyLIxLlixJ8dP/559/6GU0pooFdBxgkK8vSTUMQtWyZfm8ycT5ALubTKwZEsJ79+5lKOPhw4dZp2JFmtzc6GezceqUKQ/9TgkJCSwfEMB39XqeBbgcoJ+i8PDhwzm4UvmDvs88w5lpPKe6W62p8h8/7kjFL5E4mcuXL7OIycTNAAOgBmxLXlUaZLFw165dKWWvXbvGImZzSpyffwFWtFi4Zs2alDJ2u501ypXjLK1MIsBX3N35ar9+KWVu3rzJCePG8fn27Tlp4sSUCJaZcefOnSyFS9i0aRPr2myplONonY6DBwx4hCuTP5k1cyabKQoTtO91BaCPyZTjXMP5iYwUv9Ns/EKIuUKIq0KIYw7Hxgghjggh/hBC/CSEKOGs/iUSV1OiRAm80r8/erq7oxsAP+24D4BnY2KwZcuWlLI///wzGrq5oZ62XxTAoOhorFm2LKWMEAILVq3Cx/7+qGKzoYyi4HS1ahg7eXJKGU9PT7z9f/+HJWvXYviIEQ8kWMkIq9UKvV6fabmYmBh4pJkX8LDbEXPnTpb6yc+88OKLMNSqhVCrFS9YLAg1mfDmyJEoW7ZsXovmdJzpzjkPaia4BQ7HJpF8HwCEEIMBjAIw0IkySCQuZeLUqYDJhP2ffgo4ePQcUxR0L106Zd/HxweXARD3s2xdcneHT7FiqdqrUqUKTl++jIMHD8JqtaJSpUrO/xIOtGjRAuEkVgHoAuA0gKmKgq/79nWpHM7AaDRiw/bt2LFjB86cOYP3GzZE+fLl81oslyDUtwEnNS5EEIB1JB/I3yaEeAdAaZKDMmundu3aPHDgQO4LKJE4gXv37qF2pUqo9fff6BAXhx+NRuz298eB48dhsVgAqD71YZUqofZff6FvQgL2C4GPLRbs+eMPBAcHZ9LDw9m0aRO+XbAAFpsNr7z2GqpWrZqj9nbv3o1XnnsOV65ehZu7O97/6CMMHjYsR21KXIMQ4iDJ2g8cd7XiF0KMA/AigFsAniIZmUHdcKi5pVG6dOla58+fd5qckqwRGxuL7777DhcuXEDz5s1Rt27dvBYp33Ljxg1M++wzHNq5E9Xq18fgYcPg4+OTqsz169cx7oMPsHPzZgSHhOCdsWMRGhqao34/mTgRX40Zg8HR0bip12Oa0YiVP/6Ixo0b56hdkrh27Ro8PT1hMBhy1JbEdeQbxe9w7h0AJpIfZNaOHPHnPVFRUahVqzEiI0sgNrYqjMZv8Z//9MHEiaPzWjSJRlxcHEp4e+O3mBgkW6mXAJhbpw627NuXl6LlG+x2O7Zt24azZ8+iUaNGqFixYl6L5FQyUvx5uYBrMYBuedi/5BGYOvVL/P13Ddy9uwlJSZMRE3MQn3/+JS5cuJBrfRw6dAjDh4/EqFEfISIiItfaLSzcuHEDwm5HGYdjtQGcldcSgGqCa/XkkxjWuTP2Dh2Kp2rVwtjHfBFadnGp4hdClHPY7QTgpCv7l2SfXbt+R1xce4cjvjAYwnDkyJFcaX/evAVo1OhpfPqpARMm3EJoaF3s2bMnV9ouqJBEdHQ07FrMHH9/f/j4+GCtQ5m5ej2aNGuW7T4OHTqETi1aoEpAAHp064bIyHQts48F8775BvojR3Do7l3MjY3FkdhYfDZpUuEcZKTn45kbG4ClAK4ASABwCcDLAL4DcAzAEQBrAZTMSlvSjz/vGTVqNI3GPg7u3NdoMnnz3LlzOW47ISGBnp7FCPzu0P581qnTPBckL5js3LmToWXL0qjXs5SPDxctWECS3LFjB32tVrby8GCYzcYKpUvz0qVL2erj0KFD9DIY+AnUhPDNAPq4u+fKb54X9OnWjbPTLNjqZrNx6dKleS2a04BcwCXJCdevX2dQUCVaLG2p042gogTyzTf/L1favnbtGg0GTwJ2h//Jc/TyKpEr7Rc0oqKi6Gu1ciXU7Fr7ARZTlJRcvbdv3+aqVav4008/5Sjmfc9OnTjJQUneA+gNsEenTrn1VVzK+LFj+aJDiIZYgKUUhb///ntei+Y0MlL8MiyzJEt4e3vjzz9/w7fffqt59Sx5aCjfR6FIkSLw8fHDlSubAbQCAAixAmFh+ddrKC4uDnfv3oW3tzdEmgVOzmb9+vVoiPsTZGEABt67h6Xz56N27dqw2WzokgvBxi5EROBZh30jgEAAhw8ezHHbeUH4oEGoP2MGnr1+HWGxsVhqsaBJ69aoXr16XovmcqTil2QZRVHQp0+fXG9Xp9NhwYLp6NTpWQjREkLcgcFwGNOmbc28soshiY8//BBTJk9GUmIiggMDMW/lyhy7YT4K7u7uuJfmYXNPp4PBZMqgRvZo//zzmHj0KFoBMEENCR0BoEvDhrnaj6vw9vbG/mPHMH/ePJw9cQL/17IlOnfunNdi5QlOdefMLaQ7Z+EgMjISa9euhdlsRseOHVMWO+UnVqxYgTH9+mFddDRKAZgP4EM/P5y5fBnu7u4ukSEmJgblS5XCm1FR6EliF4ABioJfDxzIVffE+Ph4NA0Lw7EjRxAAdaLO6OGB3w4fRlBQUK71I3EeeeLHn1tIxS9xNfHx8fj2229xcM8ehNaujeeeew4mkwldW7ZE1y1b0NuhbC2bDZ+tX49GjRq5TL6TJ09i+MCB2P3bb6hQpgzGTJ2K5s2bO6WvH3/8ET/88AOqVKmC3r17w8vLyyn9SHKfjBS/NPVIJGlISkrC002bIu7IEbSPjsYiiwWzp07F1n37oFituO1QlgDukDA/JOWhM6hQoQLWZZBwJbdp27Yt2rZt69Q+1q1bh08/+gg3btxA55498fa778KUy6YryX1kBi6JJA0bNmzAzaNHsS06GiMAbI6Ohtvp01i1ahUGDBuGsYqCtQDOABji7g6v0qVRq1atPJb68WX9+vUY9OyzePXAAXwVEYGDkyej33PP5bVYTuHatWuYMG4c+vfqhUWLFmWamtNZSMUvkaThxIkTaHTvHpKDFgsATaKj8eexY2jUqBFmLl+O8ZUro5m3N6K7d8e6bdtc7tlTkPh87FhMiYlBNwD1ASy7dw8bN27EP//8k9ei5SqRkZEIq1wZp8aORY0lSzB94ED0zKPJZan4JZI01KlTB+uMRsRo+3EAfrBYUK9+fQBA+/btsfvYMVy4fh1zlixB0aJF80zWgkDUjRvwd9g3A7Dq9bh9+3ZGVR5LZkybhha3bmHuvXt4FcDW6Gjs37YNhw4dcrksUvFLJGlo0qQJGnXsiCqKggEmE0ItFlRo1szpdu7CSpcXXsB4sxl3oM6ZzBQCNj8/lCtXLrOqjxWnjx5Fvbi4lH0jgJp6Pc6cOeNyWeTkrkSSBiEEZi9ejH379uHgwYPoHRqKhg0bFgpzzsWLF7Fs2TIkJSbimR49cpwbICu8OWIETh87hlLffw+LXg9vf398u25dgbvejdq0wZJNm9AnJgZuAC4D2J6QgM/q1cusau6T3nLe/LbJkA0SSfaIiIhg5xYt6Gk2s3pwML9fvTrDsjt27KCvxcIBBgP/4+5OH7OZ69evd5mskZGRPHPmDO12u8v6dCVxcXFs16QJy1ssfNZqpbfJxMnjxzu1T2QQskH68UskBZSEhARUDAxEv3//RX+7HYcA9FUUfL91a7pJdJ4MDcXrR48i2Z9mM4DBAQE4fuFCgRt95xUksWfPHpw9exYNGzZEmTJlMq+UA+QCLomkkLFlyxa827Ur9jkkRp8sBE737o2vFyx4oLzNZMLFuDgkL8+yA3AXArH37smsW48p+TERi0QicSIJCQkwphmpG0kkxMenW75mpUr4wWF/A4BKgYEuC0UhcR1S8UskBZSnnnoKZ/V6zAGQCOAwgCmKgl79+6dbftKMGRhuteIFRUE/kwl9FAWfzZ4tzTwFEOnVI5EUUEwmEzZs24aBvXph0IkT8PXwwOgJEzKM6VOnTh0cj4jAypUrkZiYiDFdu6JkyZIullriCqTil0gKIElJSZgzezbWLFqEciEh+O/06VlySb169SoCAgJQr149+Pn5uUhaiauRph6JpAAyJDwc84YNQ9+dO1F79Wp0a9MGu3btyrB8YmIienbujNZ16uDL3r1RvnRpzJk504USS1yJHPFLJAWMyMhILF68GOfi4uAJACSsMTGYNGoUGm5NP7nNokWLcGHLFpyNiYERagC6sCFD8HTHjihWrJgLpZe4Ajnil0gKGJGRkfBxd1eVvkYIgL8vXcqwztY1a/BSdDSM2v4TABq7u2PHjh1OlFSSV0jFL5FkQkJCAsaPGYOaTzyBxtWrY/ny5Xkt0kMJCQlBotmMtdp+IoAvjEa0ekgkyMDy5XHYwVc/EcCfdjsCAwOdKqskb3CaqUcIMRdAewBXSVbRjk0C0AFAPICzAF4iedNZMkgkucHg/v1xZsUKfBEbiygAQ/r1Q1JiInr26pXXoqWLXq/H4tWr0b19ewTY7fgnMRGVa9bEO6NGZVhn4Ouvo87MmXAjUTshAfMVBSG1ayMsLMyFkktchdNW7gohGgO4C2CBg+JvBWAryUQhxEQAIPl2Zm3JlbuSvOLOnTso6euL8/HxKKId2wRgVIUK2HfiRF6KlilxcXHYv38/fHx8UKlSpUzLX7p0CV9MmYKI48fRpH179A8Ph9FozLSeJP/i8tSLJH8VQgSlOfaTw+5eAN2d1b9EkhvExsZCLwQ8HI75A7h561ZeiZRljEbjI+UBDggIwMRPP3WiRJL8Ql7a+PsB+DGjk0KIcCHEASHEgcjISBeKJZHcp2jRoqgUEoJJOh3sAGIAjDGZ0KWApgaUFA7yRPELId6FOn+0OKMyJGeSrE2ytlxIIslLFn3/PVaFhKC42YwSRiMMrVrhg3HjctRmREQExo4ejdEffohTp07lkqQSSdZwuR+/EKIv1Enf5nwcQoNKCj1lypTBvj//xIULF6AoSo5XtG7duhXPduiAngkJ0JN4ctIkzP/2W7Rr1y6XJJZIHo5LFb8Qog2AEQCakIzJrLxEkl8QQuSaa+OIQYMwKyYGyc6VbRIT8cagQWh3/nyutC+RZIbTTD1CiKUA9gAIEUJcEkK8DGAaABuAzUKIP4QQM5zVv0TiCkjixo0bSEpKynKdYxERaOGw3xzA8QsXYLfbc10+iSQ9nKb4ST5PsjhJd5IBJOeQfIJkKZLVtW2gs/qXSJzN1q1bUaFUKQQVL45Svr5YMG9eluqFVa6MVQ77qwHULFcOOp1cTylxDTJWj0SSDSIjI/FMhw5YFBODNgD+iI9Hu1dfReWqVVGrVq2H1v1k1iw83bw5NiQlQQ9gkxD4fu5cl8gtkQBS8Usk2WLdunVoIQTaavs1AITHxWHF4sWZKv6wsDCc+OsvrF69GklJSfi0SxcULVrU6TJLJMlIxS+RZAOTyYS7aUwzd/V6WBUlS/V9fHzwyiuvOEM0iSRTpFFRIskGHTt2xGF3d0wWAhcBLAKwwGDAiy+9lNeiSSSZIhW/RJINLBYLtu7Zg90tWqCOhwfm1q6N7zdtQnBwcF6LJpFkijT1SCTZpHz58lj100+ZF5RI8hlyxC+RSCSFDKn4JRKJpJAhFb9EIpEUMqTil0gkkkKGVPwSiURSyJCKXyKRSAoZTsu5m5sIISIBuDJmrS+Aay7sL6vkV7kAKVt2kbJlj/wqW36TK5DkAwkkHgvF72qEEAfSS1Cc1+RXuQApW3aRsmWP/CpbfpUrLdLUI5FIJIUMqfglEomkkCEVf/rMzGsBMiC/ygVI2bKLlC175FfZ8qtcqZA2folEIilkyBG/RCKRFDKk4pdIJJJCRqFV/EKIc0KIo0KIP4QQB9I5L4QQnwshzgghjggharpIrhBNpuTtthBiaJoyTYUQtxzKjHKiPHOFEFeFEMccjnkLITYLIU5rf4tkULePVua0EKKPi2SbJIQ4qf1mq4UQXhnUfejv7yTZPhRCXHb43dplULeNEOJ/2r030kWyLXeQ65wQ4o8M6jrtugkhSgkhtgkhjgsh/hRCDNGO5/n99hDZ8sX99siQLJQbgHMAfB9yvh2AHwEIAPUA7MsDGfUA/oG6CMPxeFMA61wkQ2MANQEcczj2XwAjtc8jAUxMp543gAjtbxHtcxEXyNYKgJv2eWJ6smXl93eSbB8CGJ6F3/wsgLIADAAOA6jkbNnSnJ8CYJSrrxuA4gBqap9tAE4BqJQf7reHyJYv7rdH3QrtiD8LdAKwgCp7AXgJIYq7WIbmAM6SdOWq5VSQ/BXAjTSHOwGYr32eD6BzOlVbA9hM8gbJKACbAbRxtmwkfyKZqO3uBRCQm31mlQyuW1aoA+AMyQiS8QCWQb3eLpFNCCEA9ACwNDf7zAokr5A8pH2+A+AEgJLIB/dbRrLll/vtUSnMip8AfhJCHBRChKdzviSAiw77l7RjruQ5ZPwPWF8IcVgI8aMQorIrhQLgT/KK9vkfAP7plMkP168f1Le29Mjs93cW/9HMAnMzMFnk9XVrBOBfkqczOO+S6yaECAJQA8A+5LP7LY1sjuTH+y1dCnPqxYYkLwshigLYLIQ4qY2E8gVCCAOAjgDeSef0Iajmn7uanfh7AOVcKV8yJCmEyHc+wUKIdwEkAlicQZG8+P2nAxgDVQmMgWpS6efkPh+V5/Hw0b7Tr5sQwgrgOwBDSd5WX0JU8vp+Syubw/H8eL9lSKEd8ZO8rP29CmA11FdsRy4DKOWwH6AdcxVtARwi+W/aEyRvk7yrfd4AwF0I4etC2f5NNntpf6+mUybPrp8Qoi+A9gB6UTOwpiULv3+uQ/Jfkkkk7QBmZdBnXl43NwBdASzPqIyzr5sQwh2qYl1McpV2OF/cbxnIlm/vt4dRKBW/EMIihLAlf4Y6QXMsTbE1AF4UKvUA3HJ43XQFGY68hBDFNFsshBB1oP6O110o2xoAyV4TfQD8kE6ZTQBaCSGKaCaNVtoxpyKEaANgBICOJGMyKJOV398ZsjnOEXXJoM/fAJQTQpTR3vqeg3q9XUELACdJXkrvpLOvm3ZPzwFwguQnDqfy/H7LSLb8fL89lLyeXc6LDarHxGFt+xPAu9rxgQAGap8FgC+helgcBVDbhfJZoCpyT4djjrL9R5P7MNQJpQZOlGUpgCsAEqDaTV8G4APgZwCnAWwB4K2VrQ1gtkPdfgDOaNtLLpLtDFRb7x/aNkMrWwLAhof9/i6QbaF2Lx2BqsyKp5VN228H1WvkrKtk047PS77HHMq67LoBaAjVDHbE4fdrlx/ut4fIli/ut0fdZMgGiUQiKWQUSlOPRCKRFGak4pdIJJJChlT8EolEUsiQil8ikUgKGVLxSyQSSSFDKn7JY4EQ4m4O6v4ihHBKAmwhxP85o92cIIToLISolNdySPIvUvFLJDkj3yl+qEHMpOKXZIhU/JLHDiHEW0KI37RgZx9px4JE6vjyw4UQH6appxNCzBNCjBVCmIQQ32gx0n8XQjylldELISYLIY5p7b8uhGgmhPjeoZ2WWuz1CQDMWoz1xdq53kKI/dqxr4UQ+nTkDxNC7NaC7O0XQtgeIk9fIcQ0h7rrhBBNtc93hRDjtHb2CiH8hRANoMZ4mqTJEJxrF15SYJCKX/JYIYRoBTUgXR0A1QHUEkI0zkJVN6gBtE6TfA/Aa1BjflWFGh5jvhDCBCAcQBCA6iRDtTrbAFQQQvhpbb0EYC7JkQBiSVYn2UsIURHAswCeJFkdQBKAXmnkN0CNhTOEZDWoYRJiHyLPw7AA2Ku18yuA/iR3Q10V/JYm19ksXBtJIUMqfsnjRitt+x1qlNIKyFpk0q+hJh4Zp+03BLAIAEieBHAeQHmoivhrajHWqcZ3J9RwC72FmmGpPtIPv9scQC0Avwk1g1VzqMv1HQkBcIXkb1r7t7W+MpLnYcQDWKd9Pgj1gSWRZEphDssseTwRAMaT/DrVQSECkHogk3a0vBvAU0KIKSTvZaPfbwCsBXAPwLe8n3wjrWzzSaYXSju7JCLj75XA+zFXkiD/nyVZRI74JY8bmwD00+KiQwhRUotx/i+AokIIHyGEEWqYXEfmANgAYIUWfngHNDOMEKI8gNIA/gc1c9MArQyEEN4AQPJvAH8DeA/qQyCZBC1cL6AGEuuuyZOcKzYwjRz/A1BcCBGmlbFlIs85ANW1+YlSyFo43ztQ0wNKJOkiFb/ksYLkTwCWANgjhDgKYCUAG8kEAKMB7IeqvE+mU/cTqCaihQBmANBpbSwH0JdkHIDZAC4AOCKEOAygp0MTiwFcJHnC4dhMrexiksehPhh+EkIc0eRIla6TajrFZwF8obW/Geoo/qsM5NkF4C8AxwF8DtW8lRnLALylTRLLyV3JA8jonBJJFtG8a34nOSevZZFIcoJU/BJJFhBCHAQQDaClNhKXSB5bpOKXSCSSQoa08UskEkkhQyp+iUQiKWRIxS+RSCSFDKn4JRKJpJAhFb9EIpEUMv4fySpd45onTs0AAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"zxjsnyNtK4EO"},"source":["### Split data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nUoL8WFkhU5J","executionInfo":{"status":"ok","timestamp":1608144481480,"user_tz":420,"elapsed":6490,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"d63d44b3-acc1-4e24-deda-298b8fb9c1a8"},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X=X, y=y, train_size=TRAIN_SIZE)\n","print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n","print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n","print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n","print (f\"Sample point: {X_train[0]} → {y_train[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X_train: (503, 2), y_train: (503,)\n","X_val: (108, 2), y_val: (108,)\n","X_test: (109, 2), y_test: (109,)\n","Sample point: [18.34509397 14.25516163] → benign\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"tTNpd_IsLO6V"},"source":["### Label encoding"]},{"cell_type":"code","metadata":{"id":"GSAMj9h6lLBo"},"source":["# Encode class labels\n","label_encoder = LabelEncoder()\n","label_encoder = label_encoder.fit(y_train)\n","num_classes = len(label_encoder.classes_)\n","y_train = label_encoder.transform(y_train)\n","y_val = label_encoder.transform(y_val)\n","y_test = label_encoder.transform(y_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"w-Wvh03BMf09","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144481481,"user_tz":420,"elapsed":6472,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"6c46da51-d8b3-4ce0-f8cd-2706623064b3"},"source":["# Class weights\n","counts = np.bincount(y_train)\n","class_weights = {i: 1.0/count for i, count in enumerate(counts)}\n","print (f\"counts: {counts}\\nweights: {class_weights}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["counts: [272 231]\n","weights: {0: 0.003676470588235294, 1: 0.004329004329004329}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"7Vk9k9dnLSDg"},"source":["### Standardize data"]},{"cell_type":"code","metadata":{"id":"-8FjM7u8llPT"},"source":["# Standardize inputs using training data\n","X_scaler = StandardScaler().fit(X_train)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"F_EdY0FLlXTc"},"source":["## Modeling"]},{"cell_type":"markdown","metadata":{"id":"90RuBAKFLj0X"},"source":["### Model"]},{"cell_type":"code","metadata":{"id":"-IZ4YOKtSCRk"},"source":["# Initialize model\n","model = MLP(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"J_2ydOvTL39a"},"source":["### Training"]},{"cell_type":"code","metadata":{"id":"bW7Tn7H_4RAp"},"source":["# Define Loss\n","class_weights_tensor = torch.Tensor(list(class_weights.values()))\n","loss_fn = nn.CrossEntropyLoss(weight=class_weights_tensor)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EKo1sfHsLq2B"},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7NBWLKDISDj8"},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-CAhJIhEs5va","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144481484,"user_tz":420,"elapsed":6438,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"284bff93-9945-4329-c594-8ace041660c8"},"source":["# Training\n","for epoch in range(NUM_EPOCHS*10):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%10==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 0.67, accuracy: 71.4\n","Epoch: 10 | loss: 0.52, accuracy: 96.8\n","Epoch: 20 | loss: 0.41, accuracy: 100.0\n","Epoch: 30 | loss: 0.33, accuracy: 100.0\n","Epoch: 40 | loss: 0.27, accuracy: 100.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"7CnVl8OzMFBL"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"id":"uGWbZlhUSFOz"},"source":["# Predictions\n","y_prob = model(X_test, apply_softmax=True)\n","y_pred = y_prob.max(dim=1)[1]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Iyw8bjgrhl3Q","executionInfo":{"status":"ok","timestamp":1608144481486,"user_tz":420,"elapsed":6424,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"12389fd4-a2a5-49a2-d11c-598c9b6fd487"},"source":["# Performance report\n","performance = get_performance(y_true=y_test, y_pred=y_pred, classes=classes)\n","print (json.dumps(performance, indent=2))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["{\n","  \"overall\": {\n","    \"precision\": 1.0,\n","    \"recall\": 1.0,\n","    \"f1\": 1.0,\n","    \"num_samples\": 109.0\n","  },\n","  \"class\": {\n","    \"benign\": {\n","      \"precision\": 1.0,\n","      \"recall\": 1.0,\n","      \"f1\": 1.0,\n","      \"num_samples\": 59.0\n","    },\n","    \"malignant\": {\n","      \"precision\": 1.0,\n","      \"recall\": 1.0,\n","      \"f1\": 1.0,\n","      \"num_samples\": 50.0\n","    }\n","  }\n","}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DmTCz8OnSFRn","colab":{"base_uri":"https://localhost:8080/","height":336},"executionInfo":{"status":"ok","timestamp":1608144481748,"user_tz":420,"elapsed":6676,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"83f11dfb-5b27-4310-a49a-80ad5f626e65"},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(8,5))\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","\n","# Sample point near the decision boundary (same point as before)\n","plt.scatter(mean_leukocyte_count+0.05, mean_blood_pressure-0.05, s=200, \n","            c='b', edgecolor='w', linewidth=2)\n","\n","# Annotate\n","plt.annotate('true: malignant,\\npred: benign',\n","             color='white',\n","             xy=(mean_leukocyte_count, mean_blood_pressure),\n","             xytext=(0.45, 0.60),\n","             textcoords='figure fraction',\n","             fontsize=16,\n","             arrowprops=dict(facecolor='white', shrink=0.1))\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x360 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"kdP98xnlbvVn"},"source":["This is a very fragile but highly realistic scenario. Based on our reduced synthetic dataset, we have achieved a model that generalized really well on the test data. But when we ask for the prediction for the same point tested earlier (which we known is malignant), the prediction is now a benign tumor. We would have completely missed the tumor. To mitigate this, we can:\n","1. Get more data around the space we are concerned about\n","2. Consume predictions with caution when they are close to the decision boundary"]},{"cell_type":"markdown","metadata":{"id":"yWzAC39adTwk"},"source":["# Takeaway"]},{"cell_type":"markdown","metadata":{"id":"8q3CiF_xF5rY"},"source":["Models are not crystal balls. So it's important that before any machine learning, we really look at our data and ask ourselves if it is truly representative for the task we want to solve. The model itself may fit really well and generalize well on your data but if the data is of poor quality to begin with, the model cannot be trusted."]},{"cell_type":"markdown","metadata":{"id":"cR45QpjQdY6N"},"source":["Once you are confident that your data is of good quality, you can finally start thinking about modeling. The type of model you choose depends on many factors, including the task, type of data, complexity required, etc. \n","\n","So once you figure out what type of model your task needs, start with simple models and then slowly add complexity. You don’t want to start with neural networks right away because that may not be right model for your data and task. Striking this balance in model complexity is one of the key tasks of your data scientists. **simple models → complex models**\n","\n"]}]}